Systems for transitioning telephony-based and in-person servicing interactions to and from an artificial intelligence (ai) chat session

ABSTRACT

A system for transitioning a telephony or in-person servicing to an artificial intelligence (AI) chat session is disclosed. The system may receive a phone call from a user device associated with a user, and transmit a voice request for personally identifiable information associated with the user. The system may also receive and authenticate the requested personally identifiable information and, in response, generate an authentication token. The system may further receive a servicing intent from the user device, and generate a corresponding servicing intent token. Also, the system may generate an API call to an AI chatbot model, transmit the authentication token and the servicing intent token to the AI chatbot model, and map the servicing intent token to a stored servicing intent. Finally, the system may transmit a message to the user device via the AI chat session.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims priority under 35U.S.C. § 120 to, U.S. patent application Ser. No. 17/340,348, filed Jun.7, 2021, which is a continuation of U.S. patent application Ser. No.16/828,996, now U.S. Pat. No. 11,032,421, filed Mar. 25, 2020, which isa continuation of U.S. patent application Ser. No. 16/419,964, now U.S.Pat. No. 10,630,840, filed May 22, 2019, the entire contents of each ofwhich are fully incorporated herein by reference.

FIELD OF INVENTION

The present disclosure generally relates to systems for transitioningbetween various types of communication mediums during a customer serviceinteraction and, more particularly, systems for seamlessly transitioninga telephony-based and in-person servicing interactions to and (sometimesfrom) an AI chat session (e.g., one or more of an short messagingservice (SMS) text-based AI chat session, a mobile applicationtext-based AI chat session, an email-based AI chat session, and a webbrowser-based AI chat session, etc.).

BACKGROUND

Organizations that offer products and/or services associated withcustomer accounts have traditionally relied on in-person servicing at abrick-and-mortar location, call centers, IVR systems to interact withcustomers for account servicing.

In person servicing at a brick-and-mortar location and call centersstaffed with human representatives can provide certain advantages,particularly for customers who wish to speak to a human. However, suchstaffing can be cost-prohibitive on the organization (and, in turn, thecustomers) and often results in long wait times for customers.

To reduce cost and increase account servicing efficiency, manyorganizations employ IVR systems. Such systems can provide customerswith requested information and perform routine account actions withouthaving to maintain a large workforce of human customer service agents.While cost effective, existing computerized customer interaction systemstend to provide an impersonal and robotic user experience, limited byscripted questions and responses, and can require a cumbersomeauthorization process for each customer-service session.

Regardless of whether an organization employs in-person servicing, callcenters, or IVR systems, these approaches utilize voice-basedcommunication that can often be disadvantageous for the customer. Spokeninformation can be easily misunderstood, difficult to follow orremember, difficult to hear (e.g., due to hearing impairments orbackground noise), and does not have a persistent record for thecustomer. In addition, a customer may be in an environment or situation(e.g., in a public place or in a meeting) where it would be inconvenientor impractical to converse with an IVR model or a human representativein voice-based communication over the phone.

As an alternative to voice-based communication systems, someorganizations have turned to text-based communication to interact withcustomers via their mobile phones. Text-based communication systemsmakes it easy and efficient to store a record of an entire customerservice interaction and can convey detailed information to a customerthat is easier to receive visually than by voice (e.g., a long accountnumber, a recent transaction history, balances in multiple accounts,etc.). Text-based communication systems are typically less likely to bemisunderstood and easy to remember because they provide a persistentrecord for the customer. However, text-based communication systems havemore difficulty addressing certain types of customer requests thanvoice-based communication system due to the nature of those requests andinformation in the related responses. Additionally, customers aretypically at the mercy of which type of communication system that theorganization has chosen to employ, and cannot select a communicationmedium that is more convenient for himself or herself. Even fororganizations that provide multiple types of communication mediums(e.g., a call center, an in-person brick-and-mortar location, and atext-based communication system) to their customers, such comprehensive“systems” operate as separate systems that cannot seamlessly transitiona customer between communication mediums. For example, a customer whotexts into a text-based communication system may have to authenticatehimself and submit his request only to find out that he must submit thattype of request to the organization's IVR system, requiring the customerto call the IVR system and both authenticate himself and submit hisrequest to that particular system. This inability to transition acustomer service interaction between communication mediums is not onlytime-consuming and irritating for the customer, but inefficientlyoveruses the organization's resources as the text-based communicationand IVR systems pass customer service interactions between one anotherand repeat completed steps.

Accordingly, there is a need for improved systems to provide efficientand cost-effective customer interaction systems for account servicing.Embodiments of the present disclosure are directed to this and otherconsiderations.

SUMMARY

Disclosed embodiments provide systems for transitioning a telephony call(e.g., an IVR call or a customer agent phone call) or an in-personservicing interaction to and (sometimes from) an AI chat session (e.g.,an SMS AI chat session, a mobile application text-based AI chat session,email AI chat session, web-based AI chat session, a phone call AI chatsession, a mobile application voice-based AI chat session, a smartspeaker application voice-based AI chat session, a vehicle entertainmentsystem application voice-based AI chat session, etc.). In an embodiment,a system is provided that includes one or more processors and a memoryin communication with the one or more processors and storinginstructions that, when executed by the one or more processors, areconfigured to cause the system to perform one or more steps.Specifically, the system may receive a phone call from a user device(e.g., a cell phone or smart device with voice capability) associatedwith a user. In response, the system may optionally (e.g., if an IVRmodel) transmit a voice request for personally identifiable informationassociated with the user over the phone call. When the user responds tothe voice request, the system in turn receives the personallyidentifiable information and can authenticate the personallyidentifiable information. In response, the system may then generate anauthentication token. The system may also receive a servicing intentfrom the user device and generate a servicing intent token based on theservicing intent. The system may then generate an applicationprogramming interface (API) call to an AI chatbot model, transmit theauthentication token and the servicing intent token to the AI chatbotmodel, and map the servicing intent token to a plurality of servicingintents stored by the AI chatbot model. Finally, system may transmit amessage (e.g., to the user device via an AI chat session. Optionally,(e.g., if previously using an IVR model) then system may transmit to theuser via the phone call, a voice message indicating that the AI chatsession is available.

In another embodiment, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to perform one or moresteps. Specifically, the system may receive a phone call from a userdevice (e.g., a cell phone or smart device with voice capability)associated with a user and, during the call, receive one or more userutterances. In response, the system may transcribe the one or more userutterances, generate an application programming interface (API) call toan AI chatbot model, and transmit the transcribed one or more userutterances to the AI chatbot model. The system may map the transcribedone or more user utterances to one or more stored servicing intenttokens from a plurality of stored servicing intent tokens, andtransmitting a message to the user device via an AI chat session (e.g.,an SMS AI chat session, a mobile application text-based AI chat session,email AI chat session, web-based AI chat session, a phone call AI chatsession, a mobile application voice-based AI chat session, a smartspeaker application voice-based AI chat session, or a vehicleentertainment system application voice-based AI chat session).

In another embodiment, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to perform one or moresteps. Specifically, the system may receive, by an IVR model and from auser device associated with a user and a phone number, a first phonecall. The system may receive, from the user device via the first phonecall, a touch tone phone input or a user utterance and determine thatthe touch tone phone input or the user utterance corresponds to a firstservicing intent. The system may then generate a first servicing intenttoken based on the first servicing intent, generate a call to an AIchatbot model via an application programming interface (API), andtransmit the first servicing intent token to the AI chatbot model. Thesystem may also map the first servicing intent token to a plurality ofservicing intents stored by the AI chatbot model, and initiate an AIchat session with the user device by transmitting a short messageservice (SMS) message, a mobile application notification, an emailmessage, or combinations thereof to the user device. The system may thentransmit, to the user device via the AI chat session, a first answerresponding to the first servicing intent. During the AI chat session,the system may receive a first user message comprising a secondservicing intent and a second user message comprising a request to betransferred to the IVR model. In response, the system may transmit thefirst user message to the IVR model and determine whether the firstphone call is active. When the first phone call is active, the systemmay transmit a system message that the IVR model is available to theuser device via the AI chat session. Alternatively, when the phone callis not active, the system may initiate, via the IVR model, a secondphone call with the first user device by calling the phone number. Thesystem may transmit, via the first phone call or the second phone call,a second answer responding to the second servicing intent.

In another embodiment, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to perform one or moresteps. Specifically, the system may receive authentication input data.In response, the system may then generate an authentication token basedon the authentication input data. The system may also receive aservicing intent input data and generate a servicing intent token basedon the servicing intent input data. The system may then generate anapplication programming interface (API) call to an AI chatbot model,transmit the authentication token and the servicing intent token to theAI chatbot model, and map the servicing intent token to a plurality ofservicing intents stored by the AI chatbot model. Finally, the systemmay transmit a message to the user device via an AI chat session (e.g.,one or more of a short messaging service (SMS) text-based AI chatsession, a mobile application text-based AI chat session, an email-basedAI chat session, and web browser-based AI chat session, voice-based AIchat session (e.g., a phone call AI chat session, a mobile applicationvoice-based AI chat session, a smart speaker application voice-based AIchat session, or a vehicle entertainment system application voice-basedAI chat session), etc.).

In another embodiment, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to perform one or moresteps. Specifically, the system may receive personally identifiableinformation from a user interaction device (e.g., a customer interactiondevice) and authenticate the personally identifiable information. Inresponse, the system may then generate an authentication token. Thesystem may also receive a servicing intent from the user interactiondevice and generate a servicing intent token based on the servicingintent. The system may then generate an application programminginterface (API) call to an AI chatbot model, transmit the authenticationtoken and the servicing intent token to the AI chatbot model, and mapthe servicing intent token to a plurality of servicing intents stored bythe AI chatbot model. Finally, the system may transmit a message to theuser device via an AI chat session (e.g., one or more of a shortmessaging service (SMS) text-based AI chat session, a mobile applicationtext-based AI chat session, an email-based AI chat session, and webbrowser-based AI chat session, voice-based AI chat session (a mobileapplication voice-based AI chat session, a smart speaker applicationvoice-based AI chat session, or a vehicle entertainment systemapplication voice-based AI chat session), etc.).

In another embodiment, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to perform one or moresteps. Specifically, the system may receive a servicing intent from auser device and generate a servicing intent token based on the servicingintent. The system may then generate an application programminginterface (API) call to an AI chatbot model, transmit the servicingintent token to the AI chatbot model, and map the servicing intent tokento a plurality of servicing intents stored by the AI chatbot model. Thesystem may transmit a message to the user device via an AI chat session(e.g., one or more of a short messaging service (SMS) text-based AI chatsession, a mobile application text-based AI chat session, an email-basedAI chat session, and web browser-based AI chat session, voice-based AIchat session, etc.). The system may transmit a request for personallyidentifiable information associated with the user from the user devicevia the AI chat session. The system may receive the personallyidentifiable information via the AI chat session and can authenticatethe personally identifiable information. Finally, the system maytransmit to the user device an answer via the AI chat session (e.g., oneor more of a short messaging service (SMS) text-based AI chat session, amobile application text-based AI chat session, an email-based AI chatsession, and web browser-based AI chat session, voice-based AI chatsession (e.g., a phone call AI chat session, a mobile applicationvoice-based AI chat session, a smart speaker application voice-based AIchat session, or a vehicle entertainment system application voice-basedAI chat session), etc.).

In another embodiment, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to perform one or moresteps. Specifically, the system may receive one or more user utterancesvia a phone call from a user device (e.g., a cell phone or smart devicewith voice capability) or from a voice recording. In response, thesystem may generate an application programming interface (API) call toan AI chatbot model and transmit the one or more user utterances to anAI chatbot model. The system may, via the AI chatbot model, transcribethe one or more user utterances. The system may map the transcribed oneor more user utterances to one or more stored servicing intent tokensfrom a plurality of stored servicing intent tokens. The system maydetermine whether an AI chat session (e.g., an SMS AI chat session, amobile application text-based AI chat session, email AI chat session,web-based AI chat session, a phone call AI chat session, a mobileapplication voice-based AI chat session, a smart speaker applicationvoice-based AI chat session, or a vehicle entertainment systemapplication voice-based AI chat session) is available for the servicingintent. The system may request a new servicing intent when the systemdetermines that the AI chat session is not available for the servicingintent. However, when the system determines that the AI chat session isavailable for the servicing intent, the system may select a messagingchannel based on explicit user preference, implicit user preference,type of information system is providing (e.g., optimal channel to handlethe specific intent), a machine learning model that predicts the bestmessaging channel for the user, or combinations thereof. Then the systemmay trigger an AI chat session and transmit a message to the userdevice, a message via the selected messaging channel.

In yet another embodiment, a system is provided that includes one ormore processors and a memory in communication with the one or moreprocessors and storing instructions that, when executed by the one ormore processors, are configured to cause the system to perform one ormore steps. Specifically, the system may receive one or more userutterances via a first phone call from a user device (e.g., a cell phoneor smart device with voice capability). In response, the system maygenerate an application programming interface (API) call to an AIchatbot model and transmit the one or more user utterances to an AIchatbot model. The system may, via the AI chatbot model, transcribe theone or more user utterances. The system may map the transcribed one ormore user utterances to one or more servicing intent tokens from aplurality of stored servicing intent tokens. The system may optionally,via a voice-based AI chat session (e.g., a phone call AI chat session, amobile application voice-based AI chat session, a smart speakerapplication voice-based AI chat session, or a vehicle entertainmentsystem application voice-based AI chat session), call the user devicevia a second phone call. Finally, the system may provide an answer tothe servicing intent token via the first phone call or the second phonecall.

Further features of the disclosed design, and the advantages offeredthereby, are explained in greater detail hereinafter with reference tospecific embodiments illustrated in the accompanying drawings, whereinlike elements are indicated with like reference designators.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and which are incorporated into andconstitute a portion of this disclosure, illustrate variousimplementations and aspects of the disclosed technology and, togetherwith the description, serve to explain the principles of the disclosedtechnology.

FIG. 1 is an example block diagram representing a system 100 that may beused for transitioning an IVR call to an AI chat session;

FIG. 2 is a component diagram 200 of an example dialogue managementsystem;

FIG. 3 is an example intelligent assistant system functionality diagram300 for providing automated natural language dialogue for accountservicing;

FIG. 4 is a flowchart of a method 400 for transitioning a telephony callto an AI chat session;

FIG. 5 is a flowchart of another method 500 for transitioning atelephony call to an AI chat session;

FIG. 6A and FIG. 6B are flowcharts of yet another method 600 fortransitioning an IVR call to an AI chat session and back to an IVR call;

FIG. 7 is a flowchart of a method 700 for transitioning in-personservicing to an AI chat session;

FIG. 8 is a flowchart of another method 800 for transitioning in-personservicing at a user interaction device to an AI chat session;

FIG. 9 is a flowchart of another method 900 for transitioningunauthenticated user to AI chat session and authenticating the user inthe AI chat session;

FIG. 10A and FIG. 10B are flowcharts of yet another method 1000 fortransitioning a user to a selected messaging channel of an AI chatsession; and

FIG. 11 is a flowchart of a method 1100 for transitioning a user fromtelephony or in-person servicing to a voice-based AI chat session.

DETAILED DESCRIPTION

Some implementations of the disclosed technology will be described morefully with reference to the accompanying drawings. This disclosedtechnology may, however, be embodied in many different forms and shouldnot be construed as limited to the implementations set forth herein. Thecomponents described hereinafter as making up various elements of thedisclosed technology are intended to be illustrative and notrestrictive. Many suitable components that would perform the same orsimilar functions as components described herein are intended to beembraced within the scope of the disclosed electronic devices andmethods. Such other components not described herein may include, but arenot limited to, for example, components developed after development ofthe disclosed technology.

It is also to be understood that the mention of one or more method stepsdoes not preclude the presence of additional method steps or interveningmethod steps between those steps expressly identified. Similarly, it isalso to be understood that the mention of one or more components in adevice or system does not preclude the presence of additional componentsor intervening components between those components expressly identified.

The disclosed technology and embodiments include systems fortransitioning an telephony (e.g., a customer service agent call or anIVR call) to an AI chat session (e.g., an short messaging service (SMS)AI chat session, a mobile application text-based AI chat session, emailAI chat session, web-based AI chat session, a mobile applicationvoice-based AI chat session, a smart speaker application voice-based AIchat session, a vehicle entertainment system application voice-based AIchat session, etc.) and sometimes back to an telephony call (e.g., anIVR call) or another type of AI chat session. In a first aspect, asystem is provided that includes one or more processors and a memory incommunication with the one or more processors and storing instructionsthat, when executed by the one or more processors, are configured tocause the system to perform one or more steps. For example, the systemmay be configured to receive, from a user device associated with a user,a phone call. The system may also transmit, to the user device via thephone call, a voice request for personally identifiable informationassociated with the user. The system may receive, from the user devicevia the phone call, the personally identifiable information andauthenticate the personally identifiable information. In response, thesystem may generate an authentication token. The system may alsoreceive, from the user device, a servicing intent and generate aservicing intent token based on the servicing intent. The system maygenerate an application programming interface (API) call to an AIchatbot model, transmit the authentication token and the servicingintent token to the AI chatbot model, and map the servicing intent tokento a plurality of servicing intents stored by the AI chatbot model. Thesystem may also transmit a message to the user device via an AI chatsession (e.g., an SMS AI chat session, a mobile application text-basedAI chat session, email AI chat session, web-based AI chat session, aphone call AI chat session, a mobile application voice-based AI chatsession, a smart speaker application voice-based AI chat session, or avehicle entertainment system application voice-based AI chat session).

In certain example implementations, the message is communicated via oneor more messaging channels comprising a short message service (SMS)message channel, a mobile application notification channel, and an emailmessage channel.

In certain example implementations, the system may be further configuredto select the one or more messaging channels based on one or more rules,predictive machine learning, or combinations thereof; and transmit, tothe user device via the phone call, a voice message indicating that theAI chat session is available.

In certain example implementations, selecting the one or more messagingchannels is based on predictive machine learning for determining one ormore implicit preference of the user based on a history of interactionswith the user.

In certain example implementations, selecting the one or more messagingchannels is based on one or more rules including determining whether theuser device has a corresponding mobile application installed, selectingthe mobile application notification channel responsive to determiningthat the user device does have the corresponding mobile applicationinstalled, selecting the email message channel responsive to determiningthat the corresponding mobile application is not installed and a user'semail is the only contact information on record, and selecting the SMSmessage channel responsive to determining that the corresponding mobileapplication is not installed and a phone number is on record. Inalternative embodiments, selecting the one or more messaging channelsincludes selecting the SMS message channel when a mobile phone number ison record even if the mobile application is installed because the userhas an implicit or explicit preference for SMS messaging.

In certain example implementations, the system may be further configuredto receive a messaging channel selection from the user device, themessaging channel selection indicative of the user's preference for oneor more messaging channels of the SMS message channel, the mobileapplication notification channel, and the email message channel.

In certain example implementations, the system may be further configuredto transmit, via the AI chat session, an answer to the user device basedon the servicing intent, and store text-based interaction comprising theanswer.

In certain example implementations, the system may be further configuredto, responsive to receiving the authentication token, transmit anindication, via the AI chat session, that the user device has beenpreviously authenticated.

In certain example implementations, the servicing intent comprises arequest for an account balance, a request for recent transactions, arequest to update an email address of the user, a request for a bankcard, a request for why a recent transaction was declined, orcombinations thereof. The answer comprises an account balance of theuser, recent transactions of the user, a prompt to the user to type anemail address of the user in the AI chat session, a confirmation that abank card will be mailed, an explanation on why the recent transactionwas declined, or combinations thereof.

In certain example implementations, the answer comprises a deep linkthat allows the user device to perform an action in a mobile applicationor a web browser based on the servicing intent and without additionalauthentication.

In certain example implementations, receiving, from the user device, theservicing intent comprises (i) receiving an option selected from a touchtone menu or (ii) receiving a user utterance corresponding to theservicing intent and determining the servicing intent from the userutterance.

In another aspect, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to perform one or moresteps recited herein. For example, the system may be configured toreceive, from a user device associated with a user, a phone call. Thesystem may also receive, from the user device via the phone call, one ormore user utterances and transcribe the one or more user utterances. Thesystem may generate an application programming interface (API) call toan AI chatbot model, transmit the transcribed one or more userutterances to the AI chatbot model, and convert the transcribed one ormore user utterances to a servicing intent recognizable by the AIchatbot model. The system may further initiate an AI chat session withthe user device by transmitting a message to the user device via the AIchat session.

In certain example implementations, the message is communicated via oneor more messaging channels comprising a short message service (SMS)message channel, a mobile application notification channel, and an emailmessage channel.

In certain example implementations, the system may be further configuredto select the one or more messaging channels based on one or more rules,predictive machine learning, or combinations thereof. The system may befurther configured to transmit, to the user device via the phone call, avoice message indicating that the AI chat session is available.

In certain example implementations, selecting the one or more messagingchannels is based on predictive machine learning for determining one ormore implicit preferences of the user based on a history of interactionswith the user.

In certain example implementations, selecting the one or more messagingchannels is based on one or more rules comprising determining whetherthe user device has a corresponding mobile application installed,selecting the mobile application notification channel responsive todetermining that the user device does have the corresponding mobileapplication installed, and selecting the email message channelresponsive to determining that the corresponding mobile application isnot installed and a user's email is the only contact information onrecord, and selecting the SMS message channel responsive to determiningthat the corresponding mobile application is not installed and a phonenumber is on record.

In certain example implementations, the system may receive a messagingchannel selection from the user device, the messaging channel selectionindicative of the user's preference for one or more messaging channelsof the SMS message channel, the mobile application notification channel,and the email message channel.

In certain example implementations, the system may be further configuredto transmit, via the AI chat session, an answer to the user device basedon the servicing intent.

In certain example implementations, the system may be further configuredto store text-based interaction comprising the answer.

In yet another aspect, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to perform one or moresteps recited herein. For example, the system may receive, by an IVRmodel and from a user device associated with a user and a phone number,a first phone call. The system may also receive, from the user devicevia the first phone call, a touch tone phone input or a user utteranceand determine that the touch tone phone input or the user utterancecorresponds to a first servicing intent. The system may generate a firstservicing intent token based on the first servicing intent. The systemmay also generate, via an application programming interface (API), acall to an AI chatbot model. The system may then transmit, to the AIchatbot model, and the first servicing intent token, and map the firstservicing intent token to a plurality of servicing intents stored by theAI chatbot model. The system may initiate an AI chat session with theuser device by transmitting a short message service (SMS) message, amobile application notification, an email message, or combinationsthereof to the user device. The system may transmit, to the user devicevia the AI chat session, a first answer responding to the firstservicing intent and receive, from the user device via the AI chatsession, a first user message comprising a second servicing intent and asecond user message comprising a request to be transferred to the IVRmodel. The system may then transmit, to the IVR model, the first usermessage and determine whether the first phone call is active. Responsiveto determining that the first phone call is active, the system maytransmit, to the user device via the AI chat session, a system messagethat the IVR model is available. Responsive to determining that thephone call is not active, the system may initiate, via the IVR model, asecond phone call with the first user device by calling the phonenumber. The system a may also transmit, via the first phone call or thesecond phone call, a second answer responding to the second servicingintent.

In an aspect, a system is provided that includes one or more processorsand a memory in communication with the one or more processors andstoring instructions that, when executed by the one or more processors,are configured to cause the system to receive authentication input data.In response, the system may then generate an authentication token basedon the authentication input data. The system may also receive aservicing intent input data and generate a servicing intent token basedon the servicing intent input data. The system may then generate anapplication programming interface (API) call to an AI chatbot model,transmit the authentication token and the servicing intent token to theAI chatbot model, and map the servicing intent token to a plurality ofservicing intents stored by the AI chatbot model. Finally, the systemmay transmit a message to the user device via an AI chat session (e.g.,an SMS AI chat session, a mobile application text-based AI chat session,email AI chat session, web-based AI chat session, a phone call AI chatsession, a mobile application voice-based AI chat session, a smartspeaker application voice-based AI chat session, a vehicle entertainmentsystem application voice-based AI chat session, etc.).

In another aspect, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to receive personallyidentifiable information from a user interaction device (e.g., acustomer interaction device) and authenticate the personallyidentifiable information. In response, the system may then generate anauthentication token. The system may also receive a servicing intentfrom the user interaction device and generate a servicing intent tokenbased on the servicing intent. The system may then generate anapplication programming interface (API) call to an AI chatbot model,transmit the authentication token and the servicing intent token to theAI chatbot model, and map the servicing intent token to a plurality ofservicing intents stored by the AI chatbot model. Finally, the systemmay transmit a message to the user device via an AI chat session (e.g.,an SMS AI chat session, a mobile application text-based AI chat session,email AI chat session, web-based AI chat session, a phone call AI chatsession, a mobile application voice-based AI chat session, a smartspeaker application voice-based AI chat session, a vehicle entertainmentsystem application voice-based AI chat session, etc.).

In another aspect, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to receive a servicingintent from a user device and generate a servicing intent token based onthe servicing intent. The system may then generate an applicationprogramming interface (API) call to an AI chatbot model, transmit theservicing intent token to the AI chatbot model, and map the servicingintent token to a plurality of servicing intents stored by the AIchatbot model. The system may transmit a message to the user device viaan AI chat session (e.g., an SMS AI chat session, a mobile applicationtext-based AI chat session, email AI chat session, web-based AI chatsession, a phone call AI chat session, a mobile application voice-basedAI chat session, a smart speaker application voice-based AI chatsession, a vehicle entertainment system application voice-based AI chatsession, etc.). The system may transmit a request for personallyidentifiable information associated with the user from the user devicevia the AI chat session. The system may receive the personallyidentifiable information via the AI chat session and can authenticatethe personally identifiable information. Finally, the system maytransmit to the user device an answer via the AI chat session (e.g., anSMS AI chat session, a mobile application text-based AI chat session,email AI chat session, web-based AI chat session, a phone call AI chatsession, a mobile application voice-based AI chat session, a smartspeaker application voice-based AI chat session, a vehicle entertainmentsystem application voice-based AI chat session, etc.).

In another aspect, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to perform one or moresteps. For example, the system may receive one or more user utterancesvia a phone call from a user device (e.g., a cell phone or smart devicewith voice capability) or from a voice recording. In response, thesystem may generate an application programming interface (API) call toan AI chatbot model and transmit the one or more user utterances to anAI chatbot model. The system may, via the AI chatbot model, transcribethe one or more user utterances. The system may map the transcribed oneor more user utterances to one or more servicing intent tokens from aplurality of stored servicing intent tokens. The system may determinewhether an AI chat session (e.g., an SMS AI chat session, a mobileapplication text-based AI chat session, email AI chat session, web-basedAI chat session, a phone call AI chat session, a mobile applicationvoice-based AI chat session, a smart speaker application voice-based AIchat session, a vehicle entertainment system application voice-based AIchat session, etc.) is available for the servicing intent. The systemmay request a new servicing intent when the system determines that theAI chat session is not available for the servicing intent. However, whenthe system determines that the AI chat session is available for theservicing intent, the system may select a messaging channel based onexplicit user preference, implicit user preference, type of informationsystem is providing (e.g., a optimal channel/medium to handle a specificintent token), machine learning predictions, or combinations thereof.Then the system may trigger an AI chat session and transmit a message tothe user device, a message via the selected messaging channel.

In yet another aspect, a system is provided that includes one or moreprocessors and a memory in communication with the one or more processorsand storing instructions that, when executed by the one or moreprocessors, are configured to cause the system to perform one or moresteps. For example, the system may receive one or more user utterancesvia a first phone call from a user device (e.g., a cell phone or smartdevice with voice capability). In response, the system may generate anapplication programming interface (API) call to an AI chatbot model andtransmit the one or more user utterances to an AI chatbot model. Thesystem may, via the AI chatbot model, transcribe the one or more userutterances. The system may map the transcribed one or more userutterances to one or more servicing stored intent tokens of a pluralityof stored servicing intents. The system may optionally, via avoice-based AI chat session (e.g., a phone call AI chat session, amobile application voice-based AI chat session, a smart speakerapplication voice-based AI chat session, a vehicle entertainment systemapplication voice-based AI chat session, etc.), call the user device viaa second phone call. Finally, the system may provide an answer to theservicing intent via the first phone call or the second phone call.

Reference will now be made in detail to example embodiments of thedisclosed technology, examples of which are illustrated in theaccompanying figures and disclosed herein. Wherever convenient, the samereferences numbers will be used throughout the drawings to refer to thesame or like parts.

FIG. 1 is an example block diagram representing a system 100 that may beused for transitioning a telephony or in-person servicing to (and from)other types of communication mediums (e.g., an SMS AI chat session, amobile application text-based AI chat session, email AI chat session,web-based AI chat session, a phone call AI chat session, a mobileapplication voice-based AI chat session, a smart speaker applicationvoice-based AI chat session, a vehicle entertainment system applicationvoice-based AI chat session etc.), according to an exampleimplementation of the disclosed technology. System 100 may be providedor controlled by an organization including, as non-limiting examples, afinancial service provider (e.g., a credit card company or a bank), atelecommunications company, an internet service provider company, ahealthcare insurance company, a doctor's practice, and a customerservice department acting on behalf of any company that provides aproduct or service that customers may inquire about. In certain exampleimplementations, the system 100 may be configured to perform one or moreof: authentication, transitioning a telephony (e.g., an IVR call or acustomer agent call) or in-person servicing to an AI chat session,enrollment in SMS account servicing, token control, and accountservicing. The system 100 utilizes automated natural language dialoguethat may adaptively respond to customer messages (regardless of thecommunication medium in which they are received) based on an evolvingcustomer context associated with a given customer. The components andarrangements shown in FIG. 1 are not intended to limit the disclosedembodiments as the components used to implement the disclosed processesand features may vary.

As shown in FIG. 1 , the system 100 may be utilized to communicate witha user device 102 via various paths, such as through a SMS aggregator104 (or gateway), which may serve as an intermediary between one or moremobile service providers 106 and the system 100. In certain exampleimplementations, the SMS aggregator 104 and/or the user device 102 maybe in communication with the system 100 via a network 108, including,but not limited to the Internet. Certain example implementations of thedisclosed technology can include a local area network 114 forcommunication with the various modules of the system.

In certain example implementations, the system 100 can include an APIgateway 110 to act as a “front door” for applications access data,logic, and/or functionality from the API server 126 and/or otherback-end services. In certain example implementations, the API gateway110 may be configured to handle tasks involved in accepting andprocessing concurrent API calls, including traffic management,authorization, access control, monitoring, API version management, etc.

In accordance with certain example implementations of the disclosedtechnology, the system 100 may be operated by an account provider andmay include one or more of: an optional authentication system 116, anoptional enrollment token store 118, an optional phone number data store120, a dialogue management system 122, a natural language processing(NLP) system 124, database 128 (which may house one or more databases),one or more web servers 130, an optional user interactiondevice/customer interaction device 140, and an optional customerrepresentative device 150. As shown, the various modules 110-150 may bein communication via the local network 114. In accordance with certainexample implementations of the disclosed technology, the optionalauthentication module 116 may be utilized to receive a user's personallyidentifiable information (e.g., e.g., first name, last name, age, sex,birthday, phone number, user name, password, address, VIP status, keycustomer status, preferences, preferred language, vehicle(s) owned,greeting name, channel, talking points (e.g., favorite sports team),bank account number, mortgage loan account number, car loan accountnumber, healthcare account number (e.g., healthcare insurance accountnumber or a lab work account number) etc.). In certain exampleimplementations, the optional authentication system 116 may extract anddecode (or receive from user device 102) the (extracted/decoded) user'spersonally identifiable information and may access the optional phonenumber data store 120, the database 128, or customer informationdatabase 216 (FIG. 2 ) to determine if the extracted personallyidentifiable information matches with any of the personally identifiableinformation stored in the optional phone number data store 120, thedatabase 128, or customer information database 216. If the personallyidentifiable information extracted, decoded, or received from userdevice 102 matches the personally identifiable information stored inoptional phone number data store 120, the database 128, or customerinformation database 216, then authentication system 116 may generate anauthentication token and may transfer that token to the dialoguemanagement system 122 or allow the dialogue management system 122 accessto the authentication token. In certain example implementations, theoptional phone number data store 120, the database 128, and customerinformation database 216 may take the form of searchable databasesand/or a look-up table that stores personally identifiable informationfor customers of the account provider. In one example implementation ofthe disclosed technology, the optional phone number data store 120 maystore mobile phone numbers for authenticated, consenting customers whoare currently enrolled in the account servicing via SMS messages. Incertain example implementations, the optional phone number data store120 may include fields with all known phone numbers of existingcustomers, and one or more fields associated with each phone number thatmay indicate a status of the phone number. In some embodiments, theoptional authentication system 116 may read both fields and authenticatethe sender's mobile phone number if there is a corresponding matchingphone number in the optional phone number data store 120, and if thestatus field indicates that the number is associated with anauthenticated and consenting customer. In accordance with certainexample implementations of the disclosed technology, the optionalenrollment token store 118 may take the form of a searchable databaseand/or a look-up table (similar to the optional phone number data store120) that stores uniquely identifiable revocable tokens for customers ofthe account provider. In one example implementation of the disclosedtechnology, the optional enrollment token store 118 may store tokens forauthenticated, consenting customers who are currently enrolled in theaccount servicing via SMS messages. In certain example implementations,the optional enrollment token store 118 may include fields with accountidentifiers for existing customers and/or one or more fields indicatinga status of the associated revocable tokens (i.e., for authenticated andconsenting customers, or otherwise). In certain example implementations,the optional authentication system 116 may read one or more fields inthe optional enrollment token store 118 to determine the status of theassociated revocable token. Additional descriptions of these and relatedprocesses involving the modules 110, 116, 118, 120, 122, 124, 126, and128, will be explained further with reference to FIGS. 2 and 3 .

In some embodiments, a customer or user may operate user device 102.User device 102 can include one or more of a mobile device, smart phone,general purpose computer, tablet computer, laptop computer, smartwearable device, voice command device, an Internet-of-Things device, asmart speaker, a vehicle entertainment (or infotainment) system, othermobile computing device, or any other device capable of communicatingwith the service provider 106 and/or the network 108, and ultimatelycommunicating with one or more components of the system 100.

According to an example implementation of the disclosed technology, theuser device 102 may belong to or be provided by a customer, or may beborrowed, rented, or shared. Customers may include individuals such as,for example, subscribers, clients, prospective clients, or customers ofan entity associated with an organization, such as individuals who haveobtained, will obtain, or may obtain a product, service, or consultationfrom an entity associated with the organization. According to someembodiments, the user device 102 may include an environmental sensor forobtaining audio or visual data, such as a microphone and/or digitalcamera, a geographic location sensor for determining the location of thedevice, an input/output device such as a transceiver for sending andreceiving data, a display for displaying digital images, one or moreprocessors including a sentiment depiction processor, and a memory incommunication with the one or more processors.

According to an example implementation of the disclosed technology, thenetwork 108 may be of any suitable type, including individualconnections via the Internet such as cellular or WiFi™ networks. In someembodiments, network 108 may connect terminals, services, and mobiledevices using direct connections such as radio-frequency identification(RFID), near-field communication (NFC), Bluetooth™, low-energyBluetooth™ (BLE), Wi-Fi™, ZigBee™ ambient backscatter communications(ABC) protocols, USB, WAN, or LAN. Bluetooth™ is a wireless technologystandard for exchange data between mobile devices and fixes devices(e.g., a local area network access point) using short wavelength UHFradio waves in industrial scientific and medical (ISM) radio bands from2.400 GHz to 2.485 GHz. Bluetooth™, divides transmitted data intopackets and transmits each packet on one of 79 designated channels. Eachchannel has a bandwidth of 1 MHz. However, BLE uses 2 MHz bandwidthaccommodating 40 channels. Wi-Fi™ is radio technology that is basedaround Institute of Electrical and Electronics Engineers (IEEE) 802.11standards and is sued for wireless local area networking. The IEEE802.11 standard provides for communication over 900 MHZ, 2.4 GHz, 5 GHz,5.9 GHz, and 60 GHz bands, which each range subdivided into multiplechannels. ZigBee™ is typically used for wireless personal area networksand is based on the IEEE 802.15.4 standard. ZigBee™ operates in the ISMradio bands of 2.4 GHz most places, 784 MHz in China, 868 MHz in Europe,and 915 MHz in USA and Australia.

Because the information transmitted may be personal or confidential,security concerns may dictate one or more of these types of connectionsbe encrypted or otherwise secured. In some embodiments, however, theinformation being transmitted may be less personal, and therefore thenetwork connections may be selected for convenience over security.

According to an example implementation of the disclosed technology, thenetwork 108 may include any type of computer networking arrangement usedto exchange data. For example, the network 108 may be the Internet, aprivate data network, virtual private network using a public network,and/or other suitable connection(s) that enables components in thesystem 100 to send and receive information between the components of thesystem 100. In certain example implementations, the network 108 may alsoinclude a public switched telephone network (“PSTN”) and/or a wirelessnetwork.

In accordance with certain example implementations of the disclosedtechnology, the system 100 may be associated with and optionallycontrolled by an entity such as a business, corporation, individual,partnership, or any other entity that provides one or more of goods,services, and consultations to individuals such as customers. The system100 can include or be in contact with one or more servers and computersystems for performing one or more functions associated with productsand/or services that an organization provides. Such servers and computersystems may include, for example, web servers, call center servers,and/or transaction servers, as well as any other computer systemsnecessary to accomplish tasks associated with the organization and/orthe needs of customers (which may be customers of the entity associatedwith the organization). In an example implementation, the system mayinclude the web server(s) 130 configured to generate and provide one ormore websites accessible to customers, as well as any other individualsinvolved in the organization normal operations.

According to an example implementation of the disclosed technology, theweb server 130 may include a computer system configured to receivecommunications from the user device 102 via for example, a mobileapplication, a chat program, an instant messaging program, avoice-to-text program, an SMS message, email, or any other type orformat of written or electronic communication. The web server 130 mayinclude one or more processors and one or more web server databases,which may be any suitable repository of website data. Information storedin web server 130 may be accessed (e.g., retrieved, updated, and addedto) via the local network 114 and/or the network 108 by one or moredevices or systems (e.g., dialogue management system 122) of the system100. In some embodiments, one or more processors may be used toimplement an automated natural language dialogue system that mayinteract with a customer via different types of communication channelssuch as a website, mobile application, instant messaging application,SMS message, email, or any other type of electronic communication. Incertain example implementations, when an incoming message is receivedfrom the user device 102, the web server 130 may be configured todetermine the type of communication channel that the user device 102used to generate the incoming message.

Certain example implementations of the system 100 may also include oneor more call center servers (not shown) that may include a computersystem configured to receive, process, and route telephone calls andother electronic communications between a customer operating user device102 and the dialogue management system 122. Information stored in callcenter server, for example may be accessed (e.g., retrieved, updated,and added to) via local network 114 and/or the network 105 by one ormore devices or systems (e.g., dialogue management system 122) of system100. In some embodiments, one or more processors may be used toimplement an interactive voice response (IVR) system that interacts withthe customer over the phone.

Certain example implementations of the system 100 may also include oneor more transaction servers (not shown) that may include a computersystem configured to process one or more transactions involving anaccount associated with customers, or a request received from customers.In some embodiments, transactions can include, for example, aproduct/service purchase, product/service return, financial transfer,financial deposit, financial withdrawal, financial credit, financialdebit, dispute request, warranty coverage request, and any other type oftransaction associated with the products and/or services that an entityassociated with the organization provides to individuals such ascustomers. The transaction server, for example, may have one or moreprocessors and one or more transaction server databases, which may beany suitable repository of transaction data. Information stored intransaction server may be accessed (e.g., retrieved, updated, and addedto) via local network 114 and/or network 108 by one or more devices orsystems (e.g., dialogue management system 122) of system 100.

In some embodiments, a transaction server may track and store event dataregarding interactions between a third party and the organization onbehalf of the customer. For example, third party interactions may betracked, which can include purchase requests, refund requests, warrantyclaims, account withdrawals and deposits, and any other type ofinteraction that a third-party server may conduct with the organizationon behalf of an individual such as customer.

In accordance with certain example implementations of the disclosedtechnology, the local network 114 may include any type of computernetworking arrangement used to exchange data in a localized area, suchas WiFi™, Bluetooth™, Ethernet, and other suitable network connectionsthat enable components of the organization to interact with one anotherand to connect to the network 108 for interacting with components of thesystem 100. In some embodiments, the local network 114 can include aninterface for communicating with or linking to the network 108. In otherembodiments, components of an organization may communicate via thenetwork 108, without a separate local network 114.

In accordance with certain example implementations of the disclosedtechnology, and with continued reference to FIG. 1 , the dialoguemanagement system 122 can include one or more computer systemsconfigured to compile data from a plurality of sources, such as the webserver 130, a call center server, and/or a transaction server. Incertain example implementations, the dialogue management system 122 maybe utilized to correlate compiled data, analyze the compiled data,arrange the compiled data, generate derived data based on the compileddata, and store the compiled and derived data in a database such asdatabase 128. According to some embodiments, database 128 may beassociated with an organization and/or its related entity, and may storea variety of information relating to customers, transactions, andbusiness operations. In certain example implementations, the database128 may also serve as a back-up storage device. In certain exampleimplementations, the database 128 may be accessed by dialogue managementsystem 122 and may be used to store records of every interaction,communication, and/or transaction a particular customer has had with thesystem 100 and/or its related entity in the past, for example, to enablethe creation of an ever-evolving customer context that may enabledialogue management system 122 to provide customized and adaptivedialogue when interacting with the customer.

In accordance with certain example implementations of the disclosedtechnology, the API server 126 may include a computer system configuredto execute one or more application program interfaces (APIs) thatprovide various functionalities related to the operations of the system100. In some embodiments, API server 126 may include API adapters thatenable the API server 126 to interface with and utilize enterprise APIsmaintained by the system 100 and/or an associated API's that may behoused on other systems or devices. In some embodiments, APIs canprovide functions that include, for example, retrieving customer accountinformation, modifying customer account information, executing atransaction related to an account, scheduling a payment, authenticatinga customer, updating a customer account to opt-in or opt-out ofnotifications, and any other such function related to management ofcustomer profiles and accounts. In certain example implementations, theAPI server 126 may include one or more processors and/or one or more APIdatabases, which may be any suitable repository of API data. In certainexample implementations, information stored in the API server 126 may beaccessed (e.g., retrieved, updated, and added to) via local network 114and/or network 108 by one or more devices or systems (e.g., dialoguemanagement system 122) of the system 100. In some embodiments, an APIprocessor may be used to implement one or more APIs that can access,modify, and retrieve customer account information. In certainembodiments, real-time APIs consistent with certain disclosedembodiments may use Representational State Transfer (REST) stylearchitecture, and in this scenario, the real time API may be called aRESTful API.

In certain embodiments, a real-time API may include a set of HypertextTransfer Protocol (HTTP) request messages and a definition of thestructure of response messages. In certain aspects, the API may allow asoftware application, which is written against the API and installed ona client (such as, for example, a transaction server) to exchange datawith a server that implements the API (such as, for example, API server126), in a request-response pattern. In certain embodiments, therequest-response pattern defined by the API may be configured in asynchronous fashion, and require that the response be provided inreal-time. In some embodiments, a response message from the server tothe client through the API consistent with the disclosed embodiments maybe in the format including, for example, Extensible Markup Language(XML), JavaScript Object Notation (JSON), and/or the like.

In some embodiments, the API design may also designate specific requestmethods for a client to access the server. For example, the client maysend GET and POST requests with parameters URL-encoded (GET) in thequery string or form-encoded (POST) in the body (e.g., a formsubmission). Additionally or alternatively, the client may send GET andPOST requests with JSON serialized parameters in the body. Preferably,the requests with JSON serialized parameters use “application/j son”content-type. In another aspect, an API design may also require theserver implementing the API return messages in JSON format in responseto the request calls from the client.

With continued reference to FIG. 1 , the system 100 may include anatural language processing system (NLP system) 124, which may include acomputer system configured to receive and process incoming dialoguemessages and determine a meaning of the incoming dialogue message. Forexample, the NLP system 124 may be configured to receive and execute acommand containing an incoming dialogue message where the commandinstructs the NLP system 124 to determine the meaning of the incomingdialogue message. The NLP system 124 may be configured to continuouslyor intermittently listen for and receive commands from a command queueto determine if there are any new commands directed to the NLP system124. Upon receiving and processing an incoming dialogue message, the NLPsystem 124 may output the meaning of an incoming dialogue message in aformat that other devices can process. For example, the NLP system 124may receive an incoming dialogue message stating “Hello, I would like toknow my account balance please,” and may determine that this statementrepresents a request for an account balance. In certain exampleimplementations, the NLP system 124 may be configured to output an eventrepresenting the meaning of the incoming dialogue message to an eventqueue for processing by another device. In some embodiments, the NLPsystem 124 may be configured to generate a natural language phrase inresponse to receiving a command. Accordingly, in some embodiments, theNLP system 124 may be configured to output an event that contains datarepresenting natural language dialogue.

In accordance with certain example implementations of the disclosedtechnology, the NLP system 124 may include one or more processors andone or more NLP databases, which may be any suitable repository of NLPdata. Information stored in the NLP system 124 may be accessed (e.g.,retrieved, updated, and added to) via local network 114 and/or network108 by one or more devices or systems (e.g., the dialogue managementsystem 122) of the system 100. In some embodiments, an NLP processor maybe used to implement an NLP system that can determine the meaning behinda string of text or voice message and convert it to a form that can beunderstood by other devices. In some embodiments, the NLP system 124includes a natural language understanding component that generates anintent token based on analyzing user utterances. In some embodiments,the NLP system includes a natural language generation component thatdetermines how the AI chat model (CBM 218) communicates language andcreates personalized responses. In some embodiments the dialoguemanagement system 122 determines what answer needs to be provided to theuser based on the intent token. This may include retrieving user datafrom the API server 126, database 128, or customer information database216. The natural language generation component of the NLP system 124takes the machine-readable abstract of the answer (e.g., provide anaccount balance, provide recent transactions, explain an accountstatus), based on the intent token and the customer context data, andconverts it to an answer that the AI chatbot model (CBM 218) delivers tothe customer (e.g., the user device 102) via the various communicationchannels (e.g., SMS messaging, etc.). The AI chatbot model (CBM 218)understands the user's natural language by mapping the utterances tointent tokens using the NLP system's 124 natural language understandingand then responds in natural language using the NLP system's 124 naturallanguage generation.

The system 100 may optionally include a user interaction device 140(e.g., a customer interaction device). The user interaction device 140may be any computing device that can communicate with the network 108 orlocal network 114 and can accept user input from a customer. The userinteraction device 140 may be a computing device that includes one ormore processors and one or more user interaction device databases. Forexample, the user interaction device 140 may be, without limitation, asmart phone, a tablet, computer kiosk, an automated teller machine(ATM), a laptop computer, a desktop computer, etc. Information stored inthe user interaction device 140 may be accessed (e.g., retrieved,updated, and added to) via local network 114 and/or network 108 by oneor more devices or systems (e.g., the dialogue management system 122and/or the NLP system 124) of the system 100.

The system 100 may optionally include a customer representative device150. The user interaction device may be any computing device that cancommunicate with the network 108 or local network 114 and can acceptuser input from a customer representative (e.g., an employee associatedwith system 100). The customer representative device 150 may be acomputing device that includes one or more processors and one or moreuser interaction device databases. For example, the customerrepresentative device 150 may be, without limitation, a smart phone, atablet, computer kiosk, an automated teller machine (ATM), a laptopcomputer, a desktop computer, etc. Information stored in the customerrepresentative device 150 may be accessed (e.g., retrieved, updated, andadded to) via local network 114 and/or network 108 by one or moredevices or systems (e.g., the dialogue management system 122 and/or theNLP system 124) of the system 100.

Although the preceding description describes various functions ofoptional customer service interaction device 150, optional userinteraction device 140, a web server 130, call center server,transaction server, dialogue management system 122, database 128, an APIserver 126, and a natural language processing (NLP) system 124, in someembodiments, some or all of these functions may be carried out by asingle computing device.

The features and other aspects and principles of the disclosedembodiments may be implemented in various environments specificallyconstructed for performing the various processes and operations of thedisclosed embodiments or they may include a general-purpose computer orcomputing platform selectively activated or reconfigured by program codeto provide the necessary functionality. Further, the processes disclosedherein may be implemented by a suitable combination of hardware,software, and/or firmware. For example, certain disclosed embodimentsmay be implemented by general purpose machines configured to executespecial software programs that perform processes consistent with thedisclosed embodiments. Alternatively, the disclosed embodiments mayimplement a specialized apparatus or system configured to executesoftware programs that perform processes consistent with the disclosedembodiments. Furthermore, although some disclosed embodiments may beimplemented by general purpose machines as computer processinginstructions, all or a portion of the functionality of the disclosedembodiments may be implemented instead in dedicated electronicshardware.

The disclosed embodiments also relate to tangible and non-transitorycomputer readable media that include program instructions or programcode that, when executed by one or more processors, perform one or morecomputer-implemented operations. The program instructions or programcode may include specially designed and constructed instructions orcode, and/or instructions and code well-known and available to thosehaving ordinary skill in the computer software arts. For example, thedisclosed embodiments may execute high level and/or low-level softwareinstructions, such as machine code (e.g., such as that produced by acompiler) and/or high-level code that can be executed by a processorusing an interpreter.

FIG. 2 is a component diagram 200 depicting additional details of theexample dialogue management system 122 (as shown in FIG. 1 ). In certainexample implementations, one or more of the modules or components 110,116, 118, 120, 122, 124, 126, 128, 130, 140, and 150 as shown anddiscussed above with reference to FIG. 1 (for example, the optionalauthentication system 116, the optional enrollment token store 118, theoptional phone number data store 120, the natural language processing(NLP) system 124, the database 128, and one or more web servers 130) maybe configured similarly as described with respect to dialogue managementsystem 122. As shown in FIG. 2 , dialogue management system 122 mayinclude a processor 202, an input/output (“I/O”) device 204, a memory206 containing an operating system (“OS”) 208 and one or more programs210. For example, dialogue management system 122 may be a single serveror may be configured as a distributed computer system including multipleservers or computers that interoperate to perform one or more of theprocesses and functionalities associated with the disclosed embodiments.In some embodiments, the dialogue management system 122 may furtherinclude a peripheral interface, a transceiver, a mobile networkinterface in communication with the processor 202, a bus configured tofacilitate communication between the various components of the dialoguemanagement system 122, and a power source configured to power one ormore components of the dialogue management system 122.

A peripheral interface may include the hardware, firmware and/orsoftware that enables communication with various peripheral devices,such as media drives (e.g., magnetic disk, solid state, or optical diskdrives), other processing devices, or any other input source used inconnection with the instant techniques. In some embodiments, aperipheral interface may include a serial port, a parallel port, ageneral purpose input and output (GPIO) port, a game port, a universalserial bus (USB), a micro-USB port, a high definition multimedia (HDMI)port, a video port, an audio port, a Bluetooth™ port, a near-fieldcommunication (NFC) port, another like communication interface, or anycombination thereof.

In some embodiments, a transceiver may be configured to communicate withcompatible devices and ID tags when they are within a predeterminedrange. A transceiver may be compatible with one or more of:radio-frequency identification (RFID), near-field communication (NFC),Bluetooth™, low-energy Bluetooth™ (BLE), Wi-Fi™, ZigBee™ ambientbackscatter communications (ABC) protocols or similar technologies.

A mobile network interface may provide access to a cellular network, theInternet, or another wide-area or local area network. In someembodiments, a mobile network interface may include hardware, firmware,and/or software that allows the processor(s) 202 to communicate withother devices via wired or wireless networks, whether local or widearea, private or public, as known in the art. A power source may beconfigured to provide an appropriate alternating current (AC) or directcurrent (DC) to power components.

The processor 202, for example, may include one or more of amicroprocessor, microcontroller, digital signal processor, co-processoror the like, or combinations thereof, capable of executing storedinstructions and operating upon stored data. The memory 206, forexample, may include, in some implementations, one or more suitabletypes of memory (e.g. such as volatile or non-volatile memory, randomaccess memory (RAM), read only memory (ROM), programmable read-onlymemory (PROM), erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), magneticdisks, optical disks, floppy disks, hard disks, removable cartridges,flash memory, a redundant array of independent disks (RAID), and thelike), for storing files including an operating system, applicationprograms (including, for example, a web browser application, a widget orgadget engine, and or other applications, as necessary), executableinstructions and data. In one embodiment, the processing techniquesdescribed herein are implemented as a combination of executableinstructions and data within the memory 206.

In certain example implementations, the processor 202 may be one or moreknown processing devices, such as, but not limited to, a microprocessorfrom the Pentium™ family manufactured by Intel™ or the Turion™ familymanufactured by AMD™. The processor 202 may constitute a single core ormultiple core processor that executes parallel processes simultaneously.For example, the processor 202 may be a single core processor that isconfigured with virtual processing technologies. In certain embodiments,the processor 202 may use logical processors to simultaneously executeand control multiple processes. The processor 202 may implement virtualmachine technologies, or other similar known technologies to provide theability to execute, control, run, manipulate, store, etc. multiplesoftware processes, applications, programs, etc. One of ordinary skillin the art would understand that other types of processor arrangementscould be implemented that provide for the capabilities disclosed herein.

According to an example implementation of the disclosed technology, thedialogue management system 122 may include one or more storage devicesconfigured to store information used by processor 202 (or othercomponents) to perform certain functions related to the disclosedembodiments. In one example the dialogue management system 122 mayinclude memory 206 that includes instructions to enable processor 202 toexecute one or more applications, such as server applications, networkcommunication processes, and any other type of application or softwareknown to be available on computer systems. Alternatively, theinstructions, application programs, etc. may be stored in an externalstorage or available from a memory over a network. The one or morestorage devices may be a volatile or non-volatile, magnetic,semiconductor, tape, optical, removable, non-removable, or other type ofstorage device or tangible computer-readable medium.

In one embodiment, dialogue management system 122 may include memory 206that includes instructions that, when executed by processor 202, performone or more processes consistent with the functionalities disclosedherein. Methods, systems, and articles of manufacture consistent withdisclosed embodiments are not limited to separate programs or computersconfigured to perform dedicated tasks. For example, dialogue managementsystem 122 may include memory 206 that may include one or more programs210 to perform one or more functions of the disclosed embodiments. Forexample, in some embodiments, dialogue management system 122 may includea rules-based platform (RBP) 222 for generating zero or more commands inresponse to processing an event, in accordance with a set of predefinedrules. In some embodiments, dialogue management system 122 may include atrained machine learning model (MLM) 224 for generating zero or morecommands in response to processing an event, in accordance with a modelthat may be continuously or intermittently updated. In some embodiments,the dialogue management system 122 may include an interactive voiceresponse (IVR) model (IVRM) 220, which voice-interaction model forinteracting with a customer via voice-based communications (e.g., aphone call). In some embodiments, the dialogue management system 122 mayinclude a chatbot model (CBM) 218 (i.e., an AI chatbot model) forinteracting with a customer via text-based or voice-based AIcommunications (e.g., an SMS AI chat session, a mobile applicationtext-based AI chat session, email AI chat session, web-based AI chatsession, a phone call AI chat session, a mobile application voice-basedAI chat session, a smart speaker application voice-based AI chatsession, a vehicle entertainment system application voice-based AI chatsession). Although not shown, in some embodiments, the dialoguemanagement system 122 may include a voice-based chatbot model andseparate a text-based chatbot model where the voice-based chatbot modelwould interact with the customer via voice (e.g., a phone call AI chatsession, a mobile application voice-based AI chat session, a smartspeaker application voice-based AI chat session, a vehicle entertainmentsystem application voice-based AI chat session) and the text-basedchatbot model would interact with the customer via text (an SMS AI chatsession, a mobile application text-based AI chat session, email AI chatsession, web-based AI chat session). In other embodiments, eachcommunication channel may have its own model (a phone call AI chatsession model, a mobile application voice-based AI chat session model, asmart speaker application voice-based AI chat session model, a vehicleentertainment system application voice-based AI chat session model, anSMS AI chat session model, a mobile application text-based AI chatsession model, email AI chat session model, and web-based AI chatsession model, etc.). Although various embodiments in this applicationdiscuss transferring from a non-AI voice-based communications forms(e.g., IVR, customer service representative, or in-person servicing at abrick-and-mortar store) to text-based communication (e.g., an SMS AIchat session, a mobile application text-based AI chat session, email AIchat session, web-based AI chat session), or to voice-basedcommunications, it is envisioned that a user may be transferred, in asimilar fashion, from any AI communication form to another AIcommunication form and from any non-AI communication form to an AIcommunication form. For example, a user may be transferred from text(e.g., an SMS AI chat session or customer representative agent chatsession) to text (e.g., an email AI chat session), voice (e.g., IVR or amobile application voice-based AI chat session) to voice (e.g., a smartspeaker application voice-based AI chat session model), text (e.g., anSMS AI chat session or customer representative agent chat session) tovoice (e.g., a phone call AI chat session), and voice (e.g., IVR,customer service representative, or in-person servicing at abrick-and-mortar store, or a mobile application voice-based AI chatsession) to text (e.g., an SMS AI chat session). Switching to andbetween the various models or communication forms within the models maybe based on user preference, a servicing intent (e.g., what can be bestcommunicated based on the servicing intent, or the AI model mayrecommend an optimal communication channel. In some embodiment, thedialogue management system 122 may communicate to the user to allcommunication channels (e.g., a phone call AI chat session, a mobileapplication voice-based AI chat session, a smart speaker applicationvoice-based AI chat session, a vehicle entertainment system applicationvoice-based AI chat session, an SMS AI chat session, a mobileapplication text-based AI chat session, email AI chat session, web-basedAI chat session). Moreover, the processor 202 may execute one or moreprograms 210 located remotely from system 100. For example, system 100may access one or more remote programs 210 (such as rules-based platform222 or trained machine learning model 224), that, when executed, performfunctions related to disclosed embodiments.

The memory 206 may include one or more memory devices that store dataand instructions used to perform one or more features of the disclosedembodiments. The memory 206 may also include any combination of one ormore databases controlled by memory controller devices (e.g., server(s),etc.) or software, such as document management systems, Microsoft™ SQLdatabases, SharePoint™ databases, Oracle™ databases, Sybase™ databases,or other relational or non-relational databases. The memory 206 mayinclude software components that, when executed by processor 202,perform one or more processes consistent with the disclosed embodiments.In some embodiments, memory 206 may include a customer informationdatabase 216 for storing related data to enable dialogue managementsystem 122 to perform one or more of the processes and functionalitiesassociated with the disclosed embodiments. The customer informationdatabase 216 may include stored data relating to a customer profile andcustomer accounts, such as for example, personally identifiableinformation (e.g., first name, last name, age, sex, birthday, phonenumber, address, VIP status, key customer status, preferences, preferredlanguage, vehicle(s) owned, greeting name, channel, talking points(e.g., favorite sports team), bank account number, mortgage loan accountnumber, car loan account number, healthcare account number (e.g.,healthcare insurance account number or a lab work account number) etc.),bank accounts, mortgage loan accounts, car loan accounts, healthcareaccount (e.g., healthcare insurance account or a lab work account),other such accounts, account numbers, authorized users associated withone or more accounts, account balances, account payment history, andother such typical account information. The customer informationdatabase 216 may further include stored data relating to previousinteractions between an organization (or its related entity) and acustomer. For example, the customer information database 216 may storecustomer interaction data that includes records of previous customerservice interactions with a customer via a website, SMS, a chat program,a mobile application, an IVR model, or notations taken after speakingwith a customer service agent. The customer information database 216 mayalso include information about business transactions between anorganization (and/or its related entity) and a customer that may beobtained from, for example, a transaction server. The customerinformation database 216 may also include customer feedback data such asan indication of whether an automated interaction with a customer wassuccessful, online surveys filled out by a customer, surveys answered bya customer following previous interactions to the account provider,digital feedback provided through websites or mobile applicationassociated with the organization or its related entity (e.g., selectinga smiley face or thumbs up to indicate approval), reviews written by acustomer, complaint forms filled out by a customer, information obtainedfrom verbal interactions with customer (e.g., information derived from atranscript of a customer service call with customer that is generatedusing, for example, voice recognition techniques) or any other types ofcommunications from a customer to the organization or its relatedentity. According to some embodiments, the functions provided by thecustomer information database 216 may also be provided by a databasethat is external to the dialogue management system 122.

In accordance with certain example implementations of the disclosedtechnology, the memory 206 may also include an event queue 212 fortemporarily storing queued events and a command queue 214 fortemporarily storing queued commands. The processor 202 may receiveevents from the event queue 212 and in response to processing the eventusing the rules-based platform 222 and/or the trained machine learningmodel 224, generate zero or more commands to be output to the commandqueue 214. According to some embodiments, dialogue management system 122may place commands in the command queue 214 in the order they aregenerated. In certain example implementations of the disclosedtechnology, the command queue 214 may be monitored to detect commandsthat are designated to be executed by the monitoring device and mayaccess pertinent commands. The event queue 212 may receive events fromother devices. According to some embodiments, events may be placed inthe event queue 212 in a first-in first-out (FIFO) order, such thatevents may then processed by the dialogue management system 122 in theorder they are received or generated.

The dialogue management system 122 may also be communicatively connectedto one or more memory devices (e.g., databases) locally or through anetwork. The remote memory devices may be configured to storeinformation and may be accessed and/or managed by dialogue managementsystem 122. By way of example, the remote memory devices may be documentmanagement systems, Microsoft™ SQL database, SharePoint™ databases,Oracle™ databases, Sybase™ databases, or other relational ornon-relational databases. Systems and methods consistent with disclosedembodiments, however, are not limited to separate databases or even tothe use of a database.

The dialogue management system 122 may also include one or more I/Odevices 204 that may include one or more interfaces for receivingsignals or input from devices and providing signals or output to one ormore devices that allow data to be received and/or transmitted bydialogue management system 122. For example, dialogue management system122 may include interface components, which may provide interfaces toone or more input devices, such as one or more keyboards, mouse devices,touch screens, track pads, trackballs, scroll wheels, digital cameras,microphones, sensors, and the like, that enable dialogue managementsystem 122 to receive data from one or more users (such as, for example,via user device 102, as discussed with reference to FIG. 1 ).

In certain embodiments of the disclosed technology, the dialoguemanagement system 122 may include any number of hardware and/or softwareapplications that are executed to facilitate any of the operations. Theone or more I/O interfaces may be utilized to receive or collect dataand/or user instructions from a wide variety of input devices. Receiveddata may be processed by one or more computer processors as desired invarious implementations of the disclosed technology and/or stored in oneor more memory devices.

While dialogue management system 122 has been described as one form forimplementing the techniques described herein, those having ordinaryskill in the art will appreciate that other, functionally equivalenttechniques may be employed. For example, as known in the art, some orall of the functionality implemented via executable instructions mayalso be implemented using firmware and/or hardware devices such asapplication specific integrated circuits (ASICs), programmable logicarrays, state machines, etc. Furthermore, other implementations of thedialogue management system 122 may include a greater or lesser number ofcomponents than those illustrated.

FIG. 3 is an example intelligent assistant system functionality diagram300 for providing automated natural language dialogue for accountservicing. Certain processes discussed with respect to FIG. 3 (asdiscussed in detail below) may be executed by the system 100, asdiscussed above with respect to FIG. 1 .

A first event may be placed in the event queue 212 in response toreceiving a customer dialogue message, for example, from the user device102. According to certain example implementations of the disclosedtechnology, a customer dialogue message may be sent using variouscommunication mediums, such as for example, SMS, a voice-to-text device,a chat application, an instant messaging application, a mobileapplication, an Internet-of-Things application, a smart speakerapplication, a vehicle entertainment system application, an IVR model,or any other such medium that may be sufficient to send and receiveelectronic communications. Responsive to the incoming customer dialogmessage, the event may be generated by, for example, a RESTful APIinterfacing with a receiving device of the system 100.

In accordance with certain example implementations of the disclosedtechnology, after the event is created, it may be placed in the eventqueue 212. An event queue 212 may be configured to temporarily store aplurality of events. According to some embodiments, events are placed inthe event queue in a first-in first-out (FIFO) manner, such that theevents will be executed in the order that they were received. In someembodiments, the event queue 212 and/or the command queue 214 may bepart of the dialogue management system 122. In some embodiments, boththe event queue 212 and the command queue 214 may be present on a deviceor component other than dialogue management system 122. For example, insome embodiments, the event queue 212 and the command queue 214 may bemaintained on a cloud server that is accessible by the dialoguemanagement system 122, the API server 126, the NLP system 124, and/orthe communication interface 301. According to some embodiments, an eventmay represent different types of information such as, for example, textreceived from a customer, voice/audio information received from acustomer, customer account information, or a request to perform someaccount-related action. For example, an event might represent a userdialogue message that has been sent to system 100 via SMS, a mobileapplication text message, or an online- or web-based AI chat sessionthat read “Hello, can you please tell me my account balance?” Accordingto some embodiments, an event may have certain metadata (such as a phonenumber and/or token) associated with it that is sufficient to allow thesystem to determine the identity of a customer associated with the eventand/or a communication medium from which the even originated.

According to some embodiments, the dialogue management system 122 maycontinuously or intermittently monitor the event queue 212. In responseto detecting an event (e.g., the first event) in the event queue, theevent may be received at the dialogue management system 122 from theevent queue 212. In some embodiments, the dialogue management system 122may include a rules-based platform, a trained machine learning model,and a customer context. According to some embodiments, the customercontext may be derived from customer information associated with aparticular customer that is stored in the system 100. For example,customer information may be stored in the optional phone number datastore 120, the optional enrollment token store 118, and/or the database128 shown in FIG. 1 and/or the database 216 shown in FIG. 2 . In someembodiments, the customer information may include one or more of accounttypes, account statuses, phone number, token, transaction history,conversation history, people models, an estimate of customer sentiment,customer goals, and customer social media information. In accordancewith certain example implementations of the disclosed technology, thesystem 100 may be configured to adapt and tailor its responses to aparticular customer based on the customer context and/or customerinformation. According to some embodiments, the customer context may beupdated each time the dialogue management system 122 receives a newevent from the event queue 212. For example, in some embodiments, thecustomer context may be updated by the dialogue management system 122responsive to receiving updated customer information.

In certain example implementations, the dialogue management system 122may, in response to processing the first event, generate a first commandto be placed in a command queue 214. According to some embodiments, thedialogue management system 122 may generate a command based on theprocessed event, the customer context, and/or customer information usingone or more of a rules-based platform 222 and a trained machine learningmodel 224 as discussed with reference to FIG. 2 . For example, in someuse cases a command may be generated using the rules-based platform 222,whereas in other use cases a command may be generated using the trainedmachine learning model 224, and further use cases may be handled by boththe rules-based platform 222 and the trained machine learning model 224working in concert. In some embodiments, the trained machine learningmodel 224 may be used as a way of enhancing the performance of therules-based platform 222 by, for example, determining which rules havepriority over other rules and what rules should be applied in a givencontext. According to some embodiments, the commands generated by thedialogue management system 122 in response to a particular event maychange as the customer context and/or customer information is updatedover time. Further, changes to the rules in the rules-based platform 222or further training of the machine learning model 224 may also result indifferent commands being generated in response to the same event.According to some embodiments, the trained machine learning model 224may be trained by updating a natural language processing device databasewith communications from customers that have been labeled using, forexample, a web user interface. Such data in the natural languageprocessing device database may undergo supervised training in a neuralnetwork model using a neural network training algorithm while the modelis offline before being deployed in the system 100.

According to some embodiments, an NLP model of the system 100 mayutilize deep learning models such as convolutional neural network (CNN)that transforms a word into a word vector and long short-term memory(LSTM) that transforms a sequence of word vectors into intent. The NLPmodel may also be trained to recognize named entities in addition tointents. For example, a named entity may include persons, places,organizations, account types, and product types. According to someembodiments, when the dialogue management system 122 generates acommand, such as a first command, it may determine an entity that willexecute the command, such as, for example, the API server 126, the NLPsystem 124, a communication interface 301, or some other device orcomponent, such that only the determined type of entity may pull thecommand from the command queue 214. For example, in the embodiment shownin FIG. 3 , the dialogue management system 122 may determine that thefirst command is to be executed by the NLP system 124 to determine themeaning of the incoming customer dialogue message. According to someembodiments, at the time the dialogue management system 122 creates anew command, the dialogue management system 122 may also update thecustomer information database 216 (and/or database 128) with informationabout a previous or concurrent transaction or customer interaction.

In accordance with certain example implementations of the disclosedtechnology, and with continued reference to FIG. 3 , the NLP system 124may receive the first command from the command queue 214, execute thecommand, and generate a second event to be placed in the event queue212. According to some embodiments, the NLP system 124 may continuouslyor intermittently monitor the command queue 214 to detect new commandsand upon detecting a new command, may receive the command from thecommand queue 214. Upon receiving a command, the NLP system 124 mayperform various functions depending on the nature of the command. Forexample, in some cases, the NLP system 124 may determine the meaning ofan incoming dialogue message in response to executing the command.According to some embodiments, the NLP system 124 may determine themeaning of an incoming dialogue message by utilizing one or more of thefollowing artificial intelligence techniques: intent classification,named entity recognition, sentiment analysis, relation extraction,semantic role labeling, question analysis, rule extraction anddiscovery, and story understanding. Intent classification may includemapping text, audio, video, and/or or other media into an intent chosenfrom a set of intents, which represent what a customer is stating,requesting, commanding, asking, or promising, in an incoming customerdialogue message. Intent classifications may include, for example, arequest for an account balance, a request to activate a credit/debitcard, an indication of satisfaction, a request to transfer funds, or anyother intent a customer may have in communicating a message. Namedentity recognition may involve identifying named entities such aspersons, places, organizations, account types, and product types intext, audio, video, or other media. Sentiment analysis may involvemapping text, audio, video, or other media into an emotion chosen from aset of emotions. For example, a set of emotions may include positive,negative, anger, anticipation, disgust, distrust, fear, happiness, joy,sadness, surprise, and/or trust. Relation extraction may involveidentifying relations between one or more named entities in text, audio,video, or other media. A relation may be for example, a “customer of”relation that indicates that a person is a customer of an organization.Semantic role labeling may involve identifying predicates along withroles that participants play in text, audio, video, or other media. Anexample of semantic role labeling may be identifying (1) the predicateEat, (2) Tim, who plays the role of Agent, and (3) orange, which playsthe role of Patient, in the sentence “Tim ate the orange.” Questionanalysis may involve performing natural language analysis on a question,including syntactic parsing, intent classification, semantic rolelabeling, relation extraction, information extraction, classifying thetype of question, and identifying what type of entity is beingrequested. Rule extraction and discovery may involve extracting generalinference rules in text, audio, video, or other media. An example ofrule extraction may be extracting the rule that “When a person turns ona light, the light will light up” from “Matt turned on the light, but itdidn't light up.” Story understanding may involve taking a story andidentifying story elements including (1) events, processes, and states,(2) goals, plans, intentions, needs, emotions, and moods of the speakerand characters in the story, (3) situations and scripts, and (4) themes,morals, and the point of the story.

In some cases, the NLP system 124 may perform natural languagegeneration in response to receiving a command. According to someembodiments, the NLP system 124 may perform natural language generationby utilizing one or more of the following artificial intelligencetechniques: content determination, discourse structuring, referringexpression generation, lexicalization, linguistic realization,explanation generation. Content determination may involve deciding whatcontent to present to the customer out of all the content that might berelevant. Discourse structuring may involve determining the order andlevel of detail in which content is expressed. Referring expressiongeneration may involve generating expressions that refer to entitiespreviously mentioned in a dialogue. Lexicalization may involve decidingwhat words and phrases to use to express a concept. Linguisticrealization may involve determining what linguistic structures, such asgrammatical constructions, to use to express an idea. Explanationgeneration may involve generating a humanly-understandable, transparentexplanation of a conclusion, chain of reasoning, or result of a machinelearning model. In the example embodiment shown in FIG. 3 , the NLPsystem 124 may determine the meaning of the incoming customer dialoguemessage and may convert it to a form that can be processed by thedialogue management system 122. Accordingly, the second event generatedby the NLP system 124 may represent a determined meaning of the incomingcustomer dialogue message and the NLP system 124 may send the secondevent to the event queue 212.

In accordance with certain example implementations of the disclosedtechnology, the dialogue management system 122 may receive the secondevent from the event queue 212. In some embodiments, the dialoguemanagement system 122 may also update the customer context by receivingupdated customer information. In response to processing the secondevent, the dialogue management system 122 may generate a second commandto be placed in a command queue 214. According to some embodiments,dialogue management system 122 may generate the second command based onthe processed event, the customer context, and/or the customerinformation using one or more of a rules-based platform 222 and atrained machine learning model 224 as described above with respect toFIG. 2 .

In the example embodiment shown in FIG. 3 , the second event mayrepresent a customer's request to know, for example, their accountbalance. Based on the customer context, customer information,rules-based platform 222 and/or trained machine learning model 224, thedialogue management system 122 may decide, for example, using predictiveanalytics that it has enough information to create a second event thatrepresents instructions to an API associated with the API server 126 tolook up the customer's account balance. However, in some embodiments,the dialogue management system 122 may decide that, for example, it istoo uncertain as to which account the customer is seeking informationabout and may instead create a second event that represents instructionsto the communication interface 301 to send a message to the user device102 requesting more information. Accordingly, based on the customercontext, the rules-based platform 222, and the trained machine learningmodel 224, the dialogue management system 122 may change or adapt itsresponses to a given request over time.

In accordance with certain example implementations of the disclosedtechnology, the API server 126 may receive the second command fromcommand queue 214, execute the command, and generate a third event to beplaced in event queue 212. According to some embodiments, the API server126 may continuously or intermittently monitor the command queue 214 todetect new commands and, upon detecting a new command, may receive thecommand from the command queue 214. Upon receiving a command, the APIserver 126 may perform various functions depending on the nature of thecommand. For example, in some cases, the API server 126 call up an APIstored locally or remotely on another device, to retrieve customer data(e.g., retrieve an account balance), perform an account action (e.g.,make a payment on a customer account), authenticate a customer (e.g.,verify customer credentials), check a status of a revocable token,and/or execute an opt-in/opt-out command (e.g., change account to opt-into paperless notifications, opt-in or opt-out of account servicing bySMS texting, etc.). Accordingly, in some embodiments, the third eventmay represent, for example, a retrieved account balance, anacknowledgement of the performance of an account action, anacknowledgement of the execution of an opt-in/opt-out command, averification or denial of a customer's credentials, a revocation of atoken, etc.

In certain example implementations, the dialogue management system 122may receive the third event from the event queue 212 in response todetecting it as described above. In some embodiments, dialoguemanagement system 122 may also update the customer context by receivingupdated customer information. The dialogue management system 122 may, inresponse to processing the third event, generate a third command to beplaced in command queue 214. According to some embodiments, dialoguemanagement system 122 may generate the third command based on theprocessed third event, the customer context, and/or customer informationusing one or more of rules-based platform 222 and trained machinelearning model 224 in a fashion similar to the generation of the firstcommand described above. In some embodiments, dialogue management system122 may also generate a response dialogue message in response toprocessing an event, such as the third event. In some embodiments,dialogue management system 122 may receive a response dialogue messageas an event produced by NLP system 124. According to some embodiments,the third command may represent a command or instruction tocommunication interface 301 to transmit the response dialogue messageto, for example, user device 102.

In certain example implementations, the communication interface 301 mayreceive and execute the third command, which may cause the communicationinterface 301 to transmit (e.g., via an SMS AI chat session, via amobile application text-based AI chat session, via an email AI chatsession, via a web-based AI chat session, via a phone call AI chatsession, via a mobile application voice-based AI chat session, via asmart speaker application voice-based AI chat session, via a vehicleentertainment system application voice-based AI chat session, etc.) theresponse dialogue message to user device 102. In some embodiments, thecommunication interface 301 may continuously or intermittently monitorthe command queue 214 for new commands and may receive the third commandin response to detecting the third command in command queue 214.According to some embodiments, the communication interface 301 may be astandalone device or system having some or all of the elements ofdialogue management system 122 as shown in FIG. 2 . In some embodiments,communication interface 301 may be integrated into dialogue managementsystem 122. In some embodiments, the communication interface 301 may beintegrated into or associated with another device or system of thesystem 100, such as, for example, the API gateway 110, the local network114, the web server 130, the API server 126, or the NLP system 124. Inaccordance with certain example implementations of the disclosedtechnology, the communication interface 301 may be configured to send“contact card” information (in the form of a SMS message, for example)to the user device 102 upon authentication and enrollment in the SMSaccount servicing. For example, the “contact card” information mayprovide a convenient way for the user device 102 to receive and store(for example, in the user's contacts) the SMS number associated with theintelligent assistant system.

As discussed with respect to FIGS. 1-3 , the system 100 may enableautomating natural language dialogue with a customer by utilizing thestructure provided by the event queue 212, dialogue management system122, command queue 214, API server 126, NLP system 124, andcommunication interface 301 to adaptively respond to customer messages.Certain example implementations of the disclosed technology may leverageartificial intelligence in the machine learning models and naturallanguage processing system to adaptively respond to customercommunications using natural language. In certain exampleimplementations, the use of a repeatedly updating customer context andinformation may enable the system 100 to generate and provide customizedresponses to individual customers and adapt the responses over time. Byutilizing artificial intelligence and machine-learning by the NLP system124, and by repeatedly updating customer context/information maintainedby the dialogue management system 122, the system may enable thenon-deterministic, adaptive, and customized conversational responses tocustomer dialogue messages. Further, according to some embodiments, thesystem 100 may enable asynchronous processing of events and creation ofcommands by the dialogue management system 122. Further, while FIG. 3and the related description appear to show a particular single cycle ofevents, it should be appreciated that multiple different cycles ofevents may be processed in parallel by the dialogue management system122.

In some embodiments, the trained machine learning model 224 (asdiscussed with reference to FIG. 2 ) may include a people model thatserves to estimate a customer's mindset per use case, and over time. Forexample, the people model may estimate how stressed out a customer isand determine, for example, how fast they want to conduct a transactionor interaction. The trained machine learning model 224 may include, forexample, a relevance measure that may quantitatively assess how relevanta particular conversion with a customer is based on the percent of taskcompletion and rate of return conversations. The trained machinelearning model 224 may include an affect recognition functionality thatseeks to recognize a customer's emotions based on facial expressions,audio speech signals, images, gestures, blood pressure, heart rate, orother such customer data that may be collected by a user device 102 andtransmitted to the system 100. In some embodiments, the trained machinelearning model 224 may include payment and financial planning featuresthat model risk factors, savings, and spending patterns over time. Insome embodiments, the trained machine learning module 224 may includeobservations of the accuracy and effectiveness of the automated naturallanguage interactions by tracking business metrics over time, such asfor example, a reduction in call center volume over a period of time. Insome embodiments, the trained machine learning module 224 may enable theexecution of hypothesis-driven micro-experiments that enable the systemto test a model hypothesis on a small population of users to validatewhether the hypotheses are valid or not.

In some embodiments, the system 100 architecture may allow the APIserver 126, the NLP system 124, and the communication interface 301 tooperate independently from one another by separately pulling commandsfrom command queue 214. In certain example implementations, the system100 may provide the advantage of asynchronous operations. Accordingly,the entire system may be stateless, with no side effects to calling aparticular function.

FIG. 4 is a flowchart of a method 400 for transitioning a telephony call(e.g., a human customer representative or an IVR call) to an AI chatsession (e.g., an SMS AI chat session, a mobile application text-basedAI chat session, email AI chat session, web-based AI chat session, aphone call AI chat session, a mobile application voice-based AI chatsession, a smart speaker application voice-based AI chat session, avehicle entertainment system application voice-based AI chat session).In certain example implementations, one or more of the steps of themethod 400 may be performed by dialogue management system 122 usingprocessor 202 to execute memory 206. In some embodiments, steps ofmethod 400 may be delegated to other components, such as the user device102, and/or components associated with the system 100. Following method400, the system 100 may engage in an AI chat session with user device102 by transmitting additional message(s) for display by user device102. Additionally, the system 100 may be configured to optionallytransition back to another communication medium (e.g., if transitionedto an SMS AI chat session via method 400, operationally transitioning toa second IVR call, a mobile application text-based AI chat session, anemail-based AI chat session, web browser-based AI chat session,) asdetermined by the system 100 based on one or more of the type ofinformation being requested, or additionally requested, by the user anduser preference(s).

In block 402, the method 400 includes receiving, from a user device 102associated with the user, a phone call. For example, the user of theuser device 102 may dial a phone number associated with system 100,thereby connecting with an IVR model 220 of the dialogue managementsystem 122.

In block 404, the method 400 optionally includes transmitting, to theuser device 102 via the phone call, a voice request for personallyidentifiable information associated with the user. For example, thedialogue management system 122 via the IVR model 220 may verballyrequest from the user, through the user device 102, the user's first andlast name and birthdate or other personally identifiable information inan attempt to identify the user. Personally identifiable information mayinclude but are not limited to first name, last name, birthdate, age,sex, birthday, social security number, address, VIP status, key customerstatus, preferences, preferred language, vehicle(s) owned, greetingname, channel, talking points (e.g., favorite sports team), etc.), bankaccounts, mortgage loan accounts, car loan accounts, healthcareaccounts, other such accounts, account numbers, authorized usersassociated with one or more accounts, account balances, account paymenthistory, and other such typical account information associated with auser, who may be a customer or member of the organization associatedwith and/or utilizing the system 100

In block 406, responsive to the request in block 404, the method 400includes receiving personally identifiable information associated withthe user. For example, if the dialogue management system 122 requestedthe first name, last name, and birthday, the user may speak into hisuser device 102 a first name, last name, and birthdate, which is thenreceived by the dialogue management system 122.

Although not shown, in some embodiments, the dialogue management system122 may determine whether the received personally identifiableinformation is complete. If the received personally identifiableinformation is incomplete (e.g., a user only supplies her first name),the dialogue management system 122 may iteratively request foradditional personally identifiable information (e.g., a last name and abirthday) until it determines that received personally indefinableinformation is sufficient to successfully identify the user via thesystem 100. If the received personally identifiable information issufficient to successfully identify the user, the dialogue managementsystem 122 may move to block 408 to authenticate the personallyidentifiable information and the additional personally identifiableinformation.

In block 408, the method 400 includes authenticating the receivedpersonally identifiable information or at least a portion thereof. Forexample, the dialogue management system 122 may compare the receivedpersonally identifiable with information associated with the identifieduser that is stored in customer information database 216 to determine ifthe information matches (e.g., beyond a predetermined confidencethreshold). If the received personally identifiable information matchesthe stored personally identifiable information, then the dialoguemanagement system 122 authenticates the personally identifiableinformation. If, however, the received personally identifiableinformation does not match any stored personally identifiableinformation associated with the identified user, the dialogue managementsystem 122 may, via the IVR model 220, request additional personallyidentifiable information or direct the user (e.g., via the user device102) to register with the dialogue management system 122 (e.g., online,via a related mobile application, or at a merchant location associatedwith the system 100). Alternatively, if the received personallyidentifiable information does not match any stored personallyidentifiable information associated with the identified user, thedialogue management system 122 may, via the IVR model 220, request toupdate some personally identifiable information online or via a relatedmobile application. It is contemplated that the system 100 may havebuilt in password recovery and reset features (e.g., with securityquestions) to allow the user to authenticate himself even when thereceived personally identifiable information does not match any storedpersonally identifiable information associated with the identified user.The system 100 may also impose limits on the number of attempts or timeduration that the user may provide personally identifiable informationand/or attempt to recover or reset his password, and such imposed limitsmay be preset based on a security level associated with the user'saccount and/or preferences provided by the user in advance of the phonecall.

In block 410, the method 400 includes generating an authentication tokenin response to authenticating the personally identifiable information.The authentication system 116 may generate the authentication token inresponse to authenticating the personally identifiable information. Theauthentication token serves as evidence that the user of user device 102is authenticated. As will be described later, this authentication tokenmay be transferred to other models (or services) (e.g., an AI chatbotmodel, a mobile application model, or another related model associatedwith another communication medium) as proof that the authenticationsystem 116 has already authenticated the user.

In block 412, the method 400 includes receiving, from the user device102, a servicing intent, which may occur in response to a prompt (e.g.,visual or audible) from the system 100 via the user device 102. Thedialogue management system 122 may receive an option selected by theuser via a user input feature (e.g., button or input feature on a touchscreen) of user device 102. For example, the user of user device 102presses 2 on a touch tone menu indicating that they are requesting anaccount balance, which is received by the dialogue management system122. In some embodiments, the NLP system 124 may receive a userutterance corresponding to the servicing intent and determine theserving intent from the user utterance. The servicing intent mayinclude, but is not limited to, add or remove a user from an account,request a refund, dispute a charge to a credit card account or bankaccount, a request for an account balance, a request for recenttransactions, a request to update an email address of the user, arequest for a bank card (e.g., a debit card or a credit card), a requestfor why a recent transaction was declined, a request for an explanationof the current account balance, a request to schedule a doctor'sappointment, a request to receive lab results, or combinations thereof.

In block 414, the method 400 includes generating a servicing intenttoken. The dialogue management system 122 generates the servicing intenttoken as evidence of the user's intent. As will be described later, thisserving intent token may be transferred to other models (or services)(e.g., an AI chatbot model) as proof that the dialogue management system122 has already received the user's intent.

In block 416, the method 400 includes generating an applicationprogramming interface (API) call to an AI chatbot model. For example,the dialogue management system 122 may call its CBM 218 in order toprepare to transmit information to the CBM 218.

In block 418, the method 400 includes transmitting, to the AI chatbotmodel, the authentication token and the servicing intent token. Forexample, the dialogue management system 122 may transmit theauthentication token to the CBM 218 so that the CBM 218 does not have toseparately authenticate the user of user device 102. That is, once auser transitions to an AI chat session, the user will not need to repeatidentification and authentication steps that were already performed withthe IVR model. Additionally, the dialogue management system 122 maytransmit to the CBM 218 the servicing intent token so that the user ofthe user device 102 does not have to explain the reason for their callagain to the CBM 218 (i.e., the AI chatbot model). This provides anexpedited user experience while not occupying system resources (e.g., inthis case the CBM 218) to repeat identification, authentication, and/orservice intent identification steps that were previously performed byanother related model (e.g., in this example the IVR model).

In block 420, the method 400 includes mapping the servicing intent tokento one or more stored servicing intent tokens from a plurality of storedservicing intent tokens. For example, the dialogue management system 122may map the servicing intent token to one or more stored servicingintent tokens of the plurality of stored servicing intent tokens. Theplurality of stored servicing intent tokens may be stored in customerinformation database 216 or database 128, and may be associated with aparticular model (e.g., in this case the AI chatbot model). In thisfashion, the servicing intent tokens recognized in one model (e.g., theIVR model) may be mapped to a similar but model-specific storedservicing intent token that can be used by another related model (e.g.,the AI chatbot model).

In block 422, the method 400 includes transmitting a message to the userdevice 102 via an AI chat session (e.g., an SMS AI chat session, amobile application text-based AI chat session, email AI chat session,web-based AI chat session, a phone call AI chat session, a mobileapplication voice-based AI chat session, a smart speaker applicationvoice-based AI chat session, a vehicle entertainment system applicationvoice-based AI chat session). For example, the dialogue managementsystem 122 may, via CBM 218, transmit a welcome or first message to theuser device 102. The welcome message may simply be “Welcome. I amworking to assist you on your issue now.” In other embodiments, thewelcome message may include identifier(s) indicative of one or more of(i) that the user has been identified (e.g., by displaying a user namein the message or elsewhere in the AI chat session display), (ii) thatthe user has been authenticated (e.g., by displaying a check next to theuser name), and (iii) the mapped stored servicing intent (e.g., addingonto or replacing part of the welcome message with “Welcome. I amworking to assist you with your balance transfer request from AccountNo. 1234 to Account No. 5678. Please confirm that you would like toproceed.”) so that the user may confirm that the mapping aligns with theuser's request. In some embodiments, the dialogue management system 122may transmit an answer (described below) to the servicing intent withthe welcome message or in lieu of the welcome message.

The welcome message may be communicated via one or more messagingchannels including a short message service (SMS) message channel, amobile application notification channel, or an email message channel. Itis contemplated that the AI chatbot model may be capable of using one ormore these communication mediums in some embodiments. In otherembodiments, a separate model (e.g., an SMS AI chatbot model, a mobileapplication AI chatbot model, and an email AI chatbot model) may bededicated to a particular communication medium such that each model isconfigured to process and generate communications of a single mediumtype. If the email message channel is used, the email sent to the userdevice 102, via a stored email address associated with the user or anemail address provided during the phone call associated with the user,may comprise a link to a web browser-based AI chatbot.

The method may include a further step (not shown) of selecting the oneor more messaging channels based on one or more rules, predictivemachine learning based on one or more user preferences (which may bestored by the system 100 or provided during the phone call) and/orservicing intent types, or combinations thereof. Selecting one or moremessaging channels may be based on one or more rules. The one or morerules may include, for example, determining whether the user device hasa corresponding mobile application installed and, if so, defaulting tocommunication via the mobile application. Or, responsive to determiningthat the user device does not have the corresponding mobile applicationinstalled, selecting the SMS message channel when the user has a storedphone number on the system 100 or selecting the email message channelwhen the user does not have a stored phone number on the system 100 butdoes have a stored email address. The rules may also include a servicingintent type associated with a user request. For example, the system maydefault to the SMS message channel for a first servicing intent type(e.g., associated with responses to the user request that can bedisplayed on single screen for a typical user device or the specificuser device 102), and default to the email message channel for a secondservicing type (e.g., associated with responses to the user request thatcannot be displayed on a single screen for a typical user device or thespecific user device 102). Selecting one or more messaging channels maybe based on predictive learning for determining one or more implicitpreferences of the user based on a history of interactions with theuser. For example, the dialogue management system 122 may have stored indatabase 128 or customer information database 216 that the past threeinteractions with the authenticated user has been over SMS messaging.Thus, the dialogue management system 122 may decide that the userprefers a conversation over SMS message channel as opposed to mobileapplication notification channel or email/Internet based channel, anddefault to using SMS messaging for future communications unless the userindicates a user preference for another communication channel before orduring the phone call (e.g., the dialogue management system 122 mayreceive a messaging channel selection from the user device 102). Themessaging channel selection may be indicative of the user's preferencefor one or more messaging channels of the SM message channel, the mobileapplication notification channel, and the email message channel.Predictive machine learning may also take into account servicing intenttypes as described above with respect to the rules (e.g., the system 100may learn that most customers are or an “average customer” is mostsatisfied when a first servicing intent type is addressed via the SMSchannel message and default to that communication medium when the userrequest is determined to be of the first servicing intent type).

In optional block 424, the method 400 may include transmitting a voicemessage, via an IVR model 220, indicating that the AI chat session isavailable. For example, the voice message may state “The chat session isavailable.” The system 100 may default to not provide the voice messageunless a response to the welcome message is not received within apredetermined time threshold. For example, if two minutes pass after thesystem 100 provides the welcome message via the user device 102, thesystem 100 may additionally provide the voice message to ensure that theuser notices the welcome message or the answer.

The method may also include transmitting, via the AI chat session usingCBM 218, an answer to the user device 102 based on the servicing intent.The answer may be providing the user device 102 with their requestedaccount balance. Other answers may include providing the user of theuser device 102 with their requested recent transactions or providingthe user of the user device 102 with an answer as to why a recenttransaction was declined (e.g., the transaction was made in a foreigncountry). The method may include storing text-based interactions orvoice-based interaction (e.g., a recording) comprising the answer incustomer information database 216 or database 128. In some embodiments,the answer includes a deep link that allows the user device 102 toperform an action in a mobile application or a web browser based on theservicing intent and without additional authentication. The deep linkmay be a hyperlink with an embedded user identifier unique to the userand an embedded request identifier unique to a particular request forthe user. For example, a user of a user device 102, who has a servicingintent of requesting her account balance, may select a deep link, sentto the user device via an SMS messaging channel, which opens to theuser's account balance on a banking website or a mobile applicationwithout the user needing to enter a username and password.

FIG. 5 is a flowchart of a method 500 for transitioning an IVR call toan AI chat session. In certain example implementations, one or more ofthe steps of the method 500 may be performed by dialogue managementsystem 122 using processor 202 to execute memory 206. In someembodiments, steps of method 500 may be delegated to other components,such as the user device 102, and/or components associated with thesystem 100. Following method 500, the system 100 may transmit a messagefor output (e.g., display or for audio/voice output) by, for example,user device 102.

In block 502, the method 500 includes receiving, from a user device 102associated with the user, a phone call as similarly described withrespect to block 402. For example, the user of the user device 102 maydial a phone number associated with system 100 thereby connecting withan IVR model 220 of the dialogue management system 122 or a livecustomer service representative.

In block 504, the method 500 includes receiving, form the user device102 via the phone call, one or more user utterances as similarlydescribed with respect to block 406. For example, a user may speak intothe user device 102 “What is my account balance?,” which may be receivedby the dialogue management system 122 via the phone call. The dialoguemanagement system 122 may record the one or more user utterances.

In block 506, the method 500 includes generating an applicationprogramming interface (API) call to an AI chatbot model as similarlydescribed with respect to block 416. For example, the dialoguemanagement system 122 may call its CBM 218 in order to prepare totransmit information to the CBM 218.

In block 508, the method 500 includes transmitting, to the AI chatbotmodel (e.g., CBM 218), the one or more user utterances. For example, thedialogue management system 122 may transmit to the CBM 218 the one ormore user utterances so that the CBM 218 does not have to separately askfor a servicing intent if the one or more user utterances include or canbe mapped to a servicing intent token (see block 512). Additionally, byreceiving the one or more user utterances, the CBM 218 receives contextfrom the conversation the user had with the IVR model 220 or a customerrepresentative agent (via phone) thereby reducing the amount of timesthe user has to repeat herself. Although different from blocks 410, 414,and 418, which describe generating and transmitting authentication andservicing into tokens to the AI chatbot model, transmitting the one ormore user utterances in block 510 may achieve one or more of theadvantages for both the customer and system 100 as described withrespect to those blocks.

In block 510, the method 500 includes transcribing the one or more userutterances. For example, the dialogue management system 122 may convertthe one or more user utterances into text that can be processed by othercomponents of the system 100. In some embodiments, the dialoguemanagement system 122 converts a voice recording of the one or more userutterances and transcribes that to text. In other embodiments, thesystem 100 includes a separate speech-to-text system (not shown) incommunication with the dialogue management system 122 that transcribesthe one or more user utterances.

In block 512, the method 500 includes mapping the transcribed one ormore user utterances to one of a plurality of stored servicing intenttokens as similarly described with respect to block 420. For example,the transcribed one or more utterances may be compared to a plurality ofstored servicing intent tokens. If the transcribed one or more userutterances matches (e.g., beyond a predetermined confidence threshold)one stored servicing intent tokens of the plurality of stored servicingintent tokens, then the dialogue management system 122 will havegenerated a serving intent token. The servicing intent token isrecognizable by the AI chatbot model (e.g., CBM 218).

Sometimes, one or more user utterances cannot be mapped to a servicingintent token because the transcribed one or more user utterances doesnot match a stored servicing intent token of the plurality of servicingintent tokens beyond a predetermined threshold (e.g., 80%). Thus, insome embodiments, the dialogue management system 122 may determinewhether the transcribed one or more user utterances was mapped to aservicing intent token. When the dialogue management system 122determines that the transcribed one or more user utterances was notmapped to a servicing intent token, the dialogue management system 122may prompt the user, via the phone call, for more informationsurrounding the purpose for the call. For example, the dialoguemanagement system in conjunction with the NLP system 124 may generate aprompt that states “What would you like to accomplish?” or “What is thepurpose for your call?” In other embodiments, when the customer or useris speaking with a human agent associated with a customer representativedevice 150, the dialogue management system 122 may transmit a prompt tothe customer representative device to instruct the human agent to askthe user for more information surrounding the purpose for the call. Themethod would then move to block 504 and repeat at least blocks 504, 506,510, 512. The dialogue management system 122 may not need to generate anew API call to the AI chatbot model, in block 508, if the API call isstill active.

In block 514, the method 500 includes transmitting a message to the userdevice 102 via an AI chat session as similarly described with respect toblock 422. For example, the dialogue management system 122 may via CBM218 transmit a welcome message to the user device 102 and/or select themessaging channel as similarly described with respect to block 422.

In optional block 516, the method 500 may include transmitting a voicemessage, via the IVR model 220 and via the phone call, indicating thatthe AI chat session is available as similarly described with respect toblock 424. For example, the voice message may state “The chat session isavailable.” In some embodiments, the method 500 may include providing acustomer representative device 150 with a prompt that the customer's (oruser's) AI chat session is available. The customer representative canthen inform the customer via the phone call that the AI chat session isavailable.

Although not shown, the method may also include transmitting, via the AIchat session using CBM 218, an answer to the user device 102 based onthe servicing intent in lieu of or in addition to providing the welcomemessage and/or the voice message. The answer may include, for example,providing the user device 102 with their requested account balance.Other answers may include providing the user of the user device 102 withtheir requested recent transactions or providing the user of the userdevice 102 with an answer as to why a recent transaction was declined(e.g., the transaction was made in a foreign country). The method mayinclude storing text-based interactions or voice-based interaction(e.g., a recording) comprising the answer in customer informationdatabase 216 or database 128. In some embodiments, the answer includes atext-based deep link that allows the user device 102 to perform anaction in a mobile application or a web browser based on the servicingintent and without additional authentication. For example, a user of auser device 102, who has a servicing intent of requesting her accountbalance, may select a deep link, sent to the user device via an SMSmessaging channel, which opens to the user's account balance on abanking website without the user needing to enter a username andpassword.

FIG. 6A and FIG. 6B are flowcharts of a method 600 for transitioning anIVR call to an AI chat session and back to an IVR call. In certainexample implementations, one or more of the steps of the method 600 maybe performed by dialogue management system 122 using processor 202 toexecute memory 206. In some embodiments, steps of method 600 may bedelegated to other components, such as the user device 102, and/orcomponents associated with the system 100. Following method 600, thesystem 100 may reestablish an IVR call with user device 102.

In block 602, the method 600 includes receiving, from a user device 102associated with a user and a phone number, a first phone call assimilarly described with respect to block 402. For example, the user ofthe user device 102 may dial a phone number associated with system 100thereby connecting with an IVR model 220 of the dialogue managementsystem 122.

In block 604, the method 600 includes receiving a touch tone phone inputor a user utterance as similarly described with respect to block 406. Insome embodiments, the dialogue management system 122 may receive anoption selected by the user of user device 102 from a touch tone menu.For example, the user of user device 102 presses 2 on a touch tone menuindicating that they are requesting an account balance, which isreceived by the dialogue management system 122. In some embodiments, thedialogue management system 122 may receive a user utterance such asspoken words (e.g., “I'd like my account balance.”).

In block 606, the method 600 includes determining that the touch tonephone input or the user utterance corresponds to a first serving intentas similarly described with respect to blocks 412 and 414. For example,the dialogue management system 122 may map the touch tone phone inputselected menu option (e.g., press 1 for account balance) to a storedservicing intent of the plurality of stored servicing intents.Alternatively, the dialogue management system 122 may map the userutterance to a stored servicing intent of the plurality of storedservicing intents as similarly described with respect to block 420. Theplurality of stored servicing intents may be stored in customerinformation database 216 or database 128.

In block 608, the method 600 includes generating a first servicingintent token based on the first servicing intent as similarly describedwith respect to block 414. The dialogue management system 122 maygenerate the first servicing intent token to characterize the user'sfirst intent based on the one or more utterances. As will be describedlater and in a similar fashion to that described with respect to block418, this serving intent token may be transferred to other models (orservices) (e.g., an AI chatbot model) as proof that the dialoguemanagement system 122 has already received the user's intent.

In block 610, the method 600 includes generating an applicationprogramming interface (API) call to an AI chatbot model as similarlydescribed with respect to block 416. For example, the dialoguemanagement system 122 may call its CBM 218 in order to prepare totransmit information to the CBM 218.

In block 612, the method 600 includes transmitting, to the AI chatbotmodel, the first servicing intent token as similarly described withrespect to block 418. For example, the dialogue management system 122may transmit to the CBM 218 the first servicing intent token so that theuser of the user device 102 does not have to explain the reason fortheir call again to the CBM 218 (i.e., the AI chatbot model).

In block 616, the method 600 includes transmitting, via an AI chatsession, a short message service (SMS) message, a mobile applicationnotification (or message), an email message (the email message may bethe AI chat session or may contain a deep link (HyperText TransferProtocol (HTTP)) to a web-based AI chat session), a web-based AI chatsession message, or combinations thereof as similarly described withrespect to block 422.

In optional block 618, the method 600 may include transmitting, to theuser device 102 via the first phone call, a first voice notificationthat the AI chat session is available as similarly described withrespect to block 424. For example, the dialogue management system 122,via IVR model 220, may “speak” to the user via the user device 102 bysaying “The chat session is available.”

In block 620, the method 600 includes transmitting, to the user device102 via the AI chat session, a first answer responding to the firstservicing intent. The system 100 may transmit the answer in lieu of orin addition to providing a welcome message and/or the voice message(e.g., transmitting the first answer in block 620 may serve as themessage in block 616). For example, the first answer could be an accountbalance in response to first servicing intent being a request for anaccount balance. The first answer may be in the form of a SMS message, amobile application notification or message, or an online AI chat sessionmessage via a website.

In block 622, the method 600 includes receiving, from the user device102 via the AI chat session, a first user message comprising (or thatthe system 100 determines comprises) a second servicing intent. Forexample, the dialogue management system 122 may receive a text-basedmessage (via a SMS messaging channel, via a mobile application messagingchannel, or via an online website AI chat session message) that requestfor the user's three most recent transactions and determine that thesecond servicing intent relates to providing recent transactions incontrast to a different first servicing intent.

In block 624, the method 600 includes receiving from the user device 122via the AI chat session, a second user message comprising a request tobe transferred to the IVR model. The dialogue management system 122 mayreceive a text-based message (e.g., an SMS message, a mobile applicationmessage, or an online AI chat session message) requesting to betransferred to voice. The message may simply state “I'd like to betransferred to a voice system” or something to that affect.

In block 626, the method 600 includes transmitting, to the IVR model220, the first user message. For example, the dialogue management system122 provides the IVR model 220 with the first message received by theuser via the AI chat session. By providing the IVR model 220 with thefirst message, the IVR model is aware of the second servicing intent andcan answer the user's request associated with the second servicingintent without requiring the user to repeat herself about the secondservicing intent. In some embodiments, the method 600 may includeiteratively transmitting additional messages received by the dialoguemanagement system 122 from the user device 102. These additionalmessages may provide context toward the second servicing intent and/oran additional servicing intent.

In block 628, the method 600 includes determining whether the firstphone call is active. For example, the dialogue management system 122may determine whether the user is still on the line with respect to thefirst phone call or whether the user hung up after transitioning to theAI chat session.

In block 630, the method 600 includes responsive to determining that thefirst phone call is active, transmitting, to the user device 102 via theAI chat session, a system message that the IVR model is available insimilar fashion to that described with respect to the message indicatingthat the AI chat session is available in block 424, only in reverse. Forexample, the response management device 122 may send a text-basedmessage to the user device 102 via an AI chat session (e.g., a SMS AIchat session, a mobile application AI chat session, or an online/websiteAI chat session) stating “Your voice session is available.”

In block 632, the method 600 includes responsive to determining that thephone call is not active, initiating via the IVR model 220, a secondphone call with the user device by calling the phone number associatedwith the user and used for the first phone call. The dialogue managementsystem 122 may store the user device phone number 122 from the firstphone call and associate it with the user in database 128 or customerinformation database 216. Since the phone number has been stored, thedialogue management system 122 via the IVR model 220 may simply call theuser device 122 based on the stored phone number. Alternatively, thesystem 100 may store a preferred phone number for the user and initiatethe second phone call with the preferred phone number regardless ofwhether that phone number was used during the first phone call.

In block 634, the method 600 includes transmitting, via the first phonecall or the second phone call, a second answer responding to the secondservicing intent. In some embodiments, the dialogue management system122 via the IVR model 220 may “speak” the second answer. For example,the dialogue management system 122 may speak the last three transactionsmade on a particular credit card if the second servicing intent requestfor the last three transactions made on a particular credit card.

FIG. 7 is a flowchart of a method 700 for transitioning in-personservicing to an AI chat session. In certain example implementations, oneor more of the steps of the method 700 may be performed by dialoguemanagement system 122 using processor 202 to execute memory 206 or theNLP system 124. In some embodiments, steps of method 700 may bedelegated to other components, such as the user device 102, and/orcomponents associated with the system 100. Following method 700, thesystem 100 may transmit a message to output by or for display by, forexample, user device 102.

In block 702, the method 700 includes receiving authentication inputdata. In some embodiments, a customer representative may authenticate acustomer by verifying the customer's identification (e.g., passport ordriver's license). For example, a customer may walk into abrick-and-mortar location (e.g., a bank, café, retail store, hospital,or doctor's office) hand over his driver's license to a customerrepresentative who authenticates that the customer is the same person inthe driver's license. The customer representative may inputauthentication data into the customer representative device 150, whichis transmitted to the dialogue management system 122. In other words,the dialogue management system 122 receives the authentication inputdata from the customer representative device 150.

In block 704, the method 700 includes generating an authentication tokenbased on the authentication input data. The authentication system 116may generate an authentication token capable of being transferred to anAI chatbot model. In some embodiments, the authentication system 116 maygenerating the authentication token in response to being called by thedialogue management system 122 via the API server 126.

In block 706, the method 700 includes receiving servicing intent inputdata. For example, a customer may describe the reason for his visit tothe brick-and-mortar location. The customer representative maysummarize, categorize, or otherwise place the customer's reason (insimplified formed) into a service intent and input related serviceintent data into to customer representative device 150, which istransmitted to the dialogue management system 122.

In block 708, the method 700 includes generating a servicing intenttoken based on the servicing intent input data. The dialogue managementsystem 122 may generate the servicing intent token capable of beingtransferred to an AI chatbot model.

In block 710, the method 700 includes generating an API call to the AIchatbot model. For example, the dialogue management system 122 may callits CBM 218 in order to prepare to transmit information to the CBM 218.The generation of the API call to the AI chatbot model may be based upona customer representative's trigger input into the customerrepresentative device 150. During the conversation with the customer,the customer may request or the customer representative may suggesttransferring to AI chat session (e.g., an SMS AI chat session, a mobileapplication text-based AI chat session, email AI chat session, web-basedAI chat session, a phone call AI chat session, a mobile applicationvoice-based AI chat session, a smart speaker application voice-based AIchat session, or a vehicle entertainment system application voice-basedAI chat session). Thus, the customer representative may trigger thetransfer by input trigger input data into the customer representativedevice 150.

In block 712, the method 700 includes transmitting, to the AI chatbotmodel, the authentication token and the servicing intent token. Forexample, the dialogue management system 122 may transmit theauthentication token to the CBM 218 so that the CBM 218 does not have toseparately authenticate the user of user device 102. That is, once auser enters an AI chat session, the user will not need to repeatidentification and authentication steps that were already performed.Additionally, the dialogue management system 122 may transmit to the CBM218 the servicing intent token so that the user of the user device 102does not have to explain the reason for walking into thebrick-and-mortar to the CBM 218 (i.e., the AI chatbot model). Thisprovides an expedited user experience while not occupying systemresources (e.g., in this case the CBM 218) to repeat identification,authentication, and/or service intent identification steps that werepreviously performed.

In block 714, the method 700 includes mapping the servicing intent tokento a stored servicing intent token from a plurality of stored servicingintent tokens. For example, the dialogue management system 122 may mapthe servicing intent token to a stored servicing intent of the pluralityof stored servicing intents. The plurality of stored servicing intentsmay be stored in customer information database 216 or database 128, andmay be associated with a particular model (e.g., in this case the AIchatbot model).

In block 716, the method 700 include transmitting a message to the userdevice via an AI chat session. The AI chat session may be is text-basedor voice-based. In some embodiments, the dialogue management system 122may transmit an SMS message, a mobile application notification ormessage, an email message, an email message containing a link (e.g., adeep link) to a web-based AI chat session, a voice message (e.g., viathe mobile application (e.g., smart phone application, tabletapplication, wearable device application (e.g., smart watchapplication), augmented/virtual reality device application)), via atelephone call, via a smart speaker application, or via a vehicleentertainment system application). In some embodiments, the dialoguemanagement system 122 may select one or more particular messagingchannels to send the message based on stored implicit preferences,explicit preferences, the type of information that the dialoguemanagement system 122 is providing (e.g., based on an optimal channelfor the specific intent), machine learning prediction of the optimalchannel, or combinations thereof.

The implicit preference may correspond to how a user has communicated inthe past with the AI chatbot model. The user's implicit preference maybe stored in database 128 or customer information database 216, whichmay be accessed by the dialogue management system 122. For example, thedialogue management system 122 may access a user's implicit preferencessuch as the majority of the user's communication with the AI chatbotmodel has been through SMS messaging. Then the dialogue managementsystem 122 may, based on the implicit preference that the user typicallycommunicates with AI chatbot model through SMS messaging, select SMSmessaging to transmit a welcome message to the user or an answer touser's servicing intent.

The explicit preference may correspond to a user selection for aparticular channel of communication. For example, the user may convey toa customer representative via the phone or at a brick-and-mortarlocation an explicit preference for communicating via a mobileapplication text-based messaging. This may be done unprompted or afterbeing prompted by the customer representative as to what communicationchannel the user chooses. The customer representative may enter thisinformation into the customer representative device 150, which istransmitted to the dialogue management system 122. In other words, thedialogue management system 122 may receive the explicit preference inputdata from the customer representative device 150. In some embodiment'sthe explicit preference overrides any implicit preference or type ofinformation to be provided considerations. Thus, if the user has anexplicit preference to communicate via a mobile application messagingand the user has an implicit preference to communicate over SMSmessaging, the dialogue management system 122 would select thecommunication channel that corresponds to the user's explicitpreference. In other words, the dialogue management system 122 willdetermine whether it has received an explicit preference regarding thecurrent session and if not, the dialogue management system 122 will baseits selection on the user's implicit preference and/or the type ofinformation that the user is providing.

The dialogue management system 122 may select a particular channel basedon the type of information that the system is providing. For example, ifservicing intent is a request for three most recent transactions on acredit card or checking account, then the dialogue management system 122may select a visual communication medium (e.g., SMS messaging channel,mobile application messaging channel, email messaging channel, web-basedmessaging channel) over a voice-based communication medium (e.g., a AIchatbot phone call and an AI chatbot mobile application voicecommunication) because it is more difficult to convey such informationover voice. Although the user explicit preferences may trump the type ofcommunication considerations, the type of information considerations maytrump a user's implicit preferences. For example, if a user typicallycommunicates with the AI chatbot model via a phone call but theservicing intent is a request for an action that is difficult to conveyvia voice (e.g., a request for an account balance, recent transactions),the dialogue management system 122 may select a visual (text-based) AIchatbot model (e.g., select SMS messaging because the user's second mostused communication medium with the AI chatbot system).

If there are no implicit preferences, explicit preferences, then thedialogue management system 122 may default to selecting SMS messaging ora mobile application messaging if the user device has the mobileapplication installed.

FIG. 8 is a flowchart of a method 800 for transitioning in-personservicing to an AI chat session. In certain example implementations, oneor more of the steps of the method 800 may be performed by dialoguemanagement system 122 using processor 202 to execute memory 206 or theNLP system 124. In some embodiments, steps of method 800 may bedelegated to other components, such as the user device 102, and/orcomponents associated with the system 100. Following method 800, thesystem 100 may transmit a message to output by or for display by, forexample, user device 102.

In block 802, the method 800 includes receive personally identifiableinformation from a user interaction device 140 or user device 102. Forexample, a user can walk into a brick-and-mortar store and interact witha user interaction device 140 (e.g., a tablet computer or a kioskcomputing device), which may prompt the user to enter personallyidentifiable information. The user can enter his personally identifiableinformation into the user interaction device 140, which is then receivedby the dialogue management system 122.

In block 804, the method 800 includes authenticating the personallyidentifiable information or at least a portion thereof. For example, theauthentication system 116 may compare the received personallyidentifiable with information associated with the identified user thatis stored in customer information database 216 or database 128 todetermine if the information matches (e.g., beyond a predeterminedconfidence threshold). If the received personally identifiableinformation matches the stored personally identifiable information, thenthe authentication system 126 authenticates the personally identifiableinformation. If, however, the received personally identifiableinformation does not match any stored personally identifiableinformation associated with the identified user, the dialogue managementsystem 122 may, via the user interaction device 140, request or promptthe user for additional personally identifiable information or directthe user (e.g., via the user device 102) to register with the dialoguemanagement system 122 (e.g., online, via a related mobile application,or at a merchant location associated with the system 100).Alternatively, if the received personally identifiable information doesnot match any stored personally identifiable information associated withthe identified user, the dialogue management system 122 may, via theuser interaction device 140, request to update some personallyidentifiable information online or via a related mobile application. Itis contemplated that the system 100 may have built in password recoveryand reset features (e.g., with security questions) to allow the user toauthenticate himself even when the received personally identifiableinformation does not match any stored personally identifiableinformation associated with the identified user. The system 100 may alsoimpose limits on the number of attempts or time duration that the usermay provide personally identifiable information and/or attempt torecover or reset his password, and such imposed limits may be presetbased on a security level associated with the user's account and/orpreferences provided by the user in advance of the interaction with theuser interaction device 140.

In block 806, the method 800 includes generating an authentication tokenin response to authenticating the personally identifiable information.Block 410 is similar to block 806, thus the description of block 410 isincorporated by reference herein.

In block 808, the method 800 includes receiving a servicing intent.Block 412 is similar to block 808, thus the description of block 412 isincorporated by reference herein.

In block 810, the method 800 includes generating a servicing intenttoken. Block 414 is similar to block 810, thus the description of block414 is incorporated by reference herein.

In block 812, the method 800 includes generating an API call to an AIchatbot model. Block 416 is similar to block 812, thus the descriptionof block 416 is incorporated by reference herein.

In block 814, the method 800 includes transmitting, to the AI chatbotmodel, the authentication token and the servicing intent token. Block418 is similar to block 814, thus the description of block 418 isincorporated by reference herein.

In block 816, the method 800 includes mapping the servicing intent tokento a stored servicing intent from a plurality of stored servicingintents. Block 420 is similar to block 816, thus the description ofblock 420 is incorporated by reference herein.

In block 818, the method 800 includes transmitting a message to the userdevice via an AI chat session. Block 422 is similar to block 818, thusthe description of block 422 is incorporated by reference herein.

FIG. 9 is a flowchart of a method 900 for transitioning unauthenticateduser to AI chat session and authenticating the user in the AI chatsession. In certain example implementations, one or more of the steps ofthe method 900 may be performed by dialogue management system 122 usingprocessor 202 to execute memory 206 or the NLP system 124. In someembodiments, steps of method 900 may be delegated to other components,such as the user device 102, and/or components associated with thesystem 100. Following method 900, the system 100 may transmit a messageto output by or for display by, for example, user device 102.

In block 902, the method 900 includes receiving a servicing intent. Thatservicing intent may be received via a phone call, through a userinteraction device 140, or through a customer representative device 150.Regardless, in one example, the servicing intent may be requesting totransfer funds from one account to another account. Blocks 412, 706, and808 are similar to block 902, thus the description of blocks 412, 706,and 808 are incorporated by reference herein.

In block 904, the method 900 includes generating a servicing intenttoken based on the servicing intent. Blocks 414, 708, and 810 aresimilar to block 904, thus the description of blocks 414, 708, and 810are incorporated by reference herein.

In block 906, the method 900 include generating an API call to an AIchatbot model. Blocks 416, 710, and 812 are similar to block 906, thusthe description of blocks 416, 710, and 812 are incorporated byreference herein.

In block 908, the method 900 includes transmitting, to the AI chatbotmodel, the servicing intent token. The dialogue management system 122may transmit to the CBM 218 the servicing intent token so that the userof the user device 102 does not have to explain the reason for walkinginto the brick-and-mortar to the CBM 218 (i.e., the AI chatbot model).This provides an expedited user experience while not occupying systemresources (e.g., in this case the CBM 218) to repeat identification,authentication, and/or service intent identification steps that werepreviously performed by another related model (e.g., in this example theIVR model).

In block 910, the method 900 includes mapping the servicing intent tokento a stored servicing intent from a plurality of stored servicingintents. The dialogue management 122 may map the servicing intent tokento a stored servicing intent of the plurality of stored servicingintents. In this fashion, the servicing intent token recognized in onemodel (e.g., an IVR model, interaction with a customer service agent, orinteraction with a user interaction device) may be mapped to a similarbut model-specific stored servicing intent that can be used by anotherrelated model (e.g., the AI chatbot model).

In block 912, the method 900 includes transmitting a message to the userdevice via an AI chat session. Blocks 422, 716, and 818 are similar toblock 912, thus the description of blocks 422, 716, and 818 areincorporated by reference herein.

In block 914, the method 900 includes transmitting a request forpersonally identifiable information associated with the user from theuser device via the AI chat session whether it be an SMS AI chatsession, a mobile application text-based AI chat session, email AI chatsession, web-based AI chat session, a phone call AI chat session, amobile application voice-based AI chat session, a smart speakerapplication voice-based AI chat session, or a vehicle entertainmentsystem application voice-based AI chat session.

In block 916, the method 900 includes receiving the personallyidentifiable information via the AI chat session. The dialoguemanagement system 122 receives the personally identifiable informationvia the AI chat session whether it be an SMS AI chat session, a mobileapplication text-based AI chat session, email AI chat session, web-basedAI chat session, a phone call AI chat session, and a mobile applicationvoice-based AI chat session. If the AI chat session is text-based, thenthe dialogue management system 122 may simply receive and process thepersonally identifiable information. If the AI chat session is voicebased (e.g., a phone call AI chat session or a mobile applicationvoice-based AI chat session), the dialogue management system 122 mayreceive one or more user utterances which may be transcribed by NLPsystem 124 and interpreted as personally identifiable information.Although not shown, in some embodiments, the dialogue management system122 may determine whether the received personally identifiableinformation is complete. If the received personally identifiableinformation is incomplete (e.g., a user only supplies her first name),the dialogue management system 122 may iteratively request foradditional personally identifiable information (e.g., a last name and abirthday) until it determines that received personally indefinableinformation is sufficient or complete to successfully identify the uservia the system 100. If the received personally identifiable informationis sufficient or complete to successfully identify the user, thedialogue management system 122 may move to block 918 to authenticate thepersonally identifiable information and the additional personallyidentifiable information.

In block 918, the method 900 includes authenticating the personallyidentifiable information via the AI chat session. Blocks 408 and 804 aresimilar to block 918, thus the description of blocks 408 and 804 areincorporated by reference herein. However, unlike blocks 408 and 804,the dialogue management system 122 is authenticating the personallyidentifiable information via the AI chatbot model (i.e., CBM 218).

In block 920, the method 900 includes transmitting to the user device ananswer via the AI chat session. Because the user is now authenticated,the dialogue management system 122 may transmit to the user device ananswer to the servicing intent. For example, if the servicing intent isto obtain an account balance, then the dialogue management system 122may transmit the user's account balance via the AI chat session whetherit be an SMS AI chat session, a mobile application text-based AI chatsession, email AI chat session, web-based AI chat session, an phone callAI chat session, and a mobile application voice-based AI chat session.

FIGS. 10A and 10B are flowcharts of a method 1000 for transitioning auser to a selected messaging channel of an AI chat session. In certainexample implementations, one or more of the steps of the method 1000 maybe performed by dialogue management system 122 using processor 202 toexecute memory 206 or the NLP system 124. In some embodiments, steps ofmethod 1000 may be delegated to other components, such as the userdevice 102, and/or components associated with the system 100. Followingmethod 1000, the system 100 may transmit a message to output by or fordisplay by, for example, user device 102.

In block 1002, the method 1000 includes receiving one or more userutterances. For example, a user may speak into the user device 102 “Whatis my account balance?,” which may be received by the dialoguemanagement system 122 via a phone call. In some embodiments, the one ormore user utterances are recorded in-person or over the telephone andthen transmitted to the dialogue management system 122.

In block 1004, the method 1000 includes generating an API call to an AIchatbot model. Blocks 416, 506, 610, 710, 812, 906 are similar to block1004, thus blocks 416, 506, 610, 710, 812, 906 description isincorporated by reference herein.

In block 1006, the method 1000 includes transmitting, to the AI chatbotmodel, the one or more user utterances. The dialogue management system122 may transmit, to the AI chatbot model (CBM 218) the one or more userutterances.

In block 1008, the method 1000, includes transcribing the one or moreuser utterances via the AI chatbot model. The NLP system 124 and the AIchatbot model may transcribe the one or more user utterances.

In block 1010, the method 1000 includes mapping the transcribed one ormore user utterances to one or more servicing intent tokens from aplurality of stored servicing intent tokens as similarly described withrespect to blocks 420 and 512 except that the AI chatbot model and theNLP system 124 may be involved in the mapping process.

In determination block 1012, the method 1000 includes determiningwhether an AI chat session is available for the servicing intent.Assuming that the dialogue management system 122 was able to map the oneor more user utterances to one or more servicing intent tokens from aplurality of stored servicing intent tokens, the dialogue managementsystem 122 may determine whether an AI chat session is available for theparticular one or more servicing intent tokens. The dialogue managementsystem 122 may compare to the one or more servicing intent tokens to alist of stored servicing intent tokens that the AI chatbot model cannothandle due to security, policy, or other reasons. For example, a usermay have a servicing intent to transfer a large sum of money (e.g.,$20,000) from a bank associated with the AI chatbot model to a bank thatis not associated with the AI chatbot model. However, the AI chatbotmodel may have a policy transfer limit of $5,000 to outside banks viathe AI chatbot model to avoid fraudulent transfers. As another example,the AI chatbot model may not be able to wire money of $10,000 or more toa foreign company or bank to avoid fraud. As a further example, the AIchatbot model may not be able to cancel a subscription, credit card, orbank account due to policy reasons (e.g., the company wants you to speakwith a person to convince you to not cancel before canceling).

In response to determining that an AI chat session is not available forthe servicing intent, the method 1000, in block 1014, includesrequesting a new servicing intent. In an alternative embodiment, inresponse to determining that an AI chat session is not available for theservicing intent, the method 1000, includes recommend that the customerresume connecting with or connect with a customer representative. Thedialogue management system 122 may prompt the user, via a new phone callor an already established phone call, for more information surroundingthe user's purpose for calling. For example, the dialogue managementsystem in conjunction with the NLP system 124 may generate a prompt thatstates “What would you like to accomplish?” or “What is the purpose foryour call?” In other embodiments, when the customer or user is speakingwith a human agent (in-person or over the phone) associated with acustomer representative device 150, the dialogue management system 122may transmit a prompt to the customer representative device to instructthe human agent to ask the user for more information surrounding thepurpose for the call. The method would then move to block 1002 andrepeat at least blocks 1002, 1006, 1008, 1010, and 1012. The dialoguemanagement system 122 may not need to generate a new API call to the AIchatbot model, in block 1004, if the API call is still active.

In response to determining that an AI chat session is available for theserving intent, the method 1000, in block 1016, includes selecting amessaging channel (e.g., SMS messaging channel, mobile applicationmessaging channel, email messaging channel, web-based messaging channel,an AI chatbot phone call and an AI chatbot mobile application voicecommunication, etc.) based on explicit user preferences (e.g., where thesystem receives the explicit user preference via one or more userutterances or the user selects a messaging channel in response to aprompt (verbal or textual or other display prompt) by the dialoguemanagement system 122, which is received by the dialogue managementsystem 122), implicit user preference (e.g., based on previous customerinteractions is typically used when other information (e.g., explicitpreferences) in not available), type of information the system isproviding (e.g., based on an optimal channel for the specific intent),machine learning prediction of the optimal channel (e.g., when broaderinformation is available such as implicit preferences, implicitpreferences, contextual (e.g., type of information the system isproviding), and other data related to the customer, or combinationsthereof. In some embodiments, the dialogue management system 122 maysimply select all available messaging channels to send a message basedon specific requirements or customer need.

The implicit preference may correspond to how a user has communicated inthe past with the AI chatbot model. The user's implicit preference maybe stored in database 128 or customer information database 216, whichmay be accessed by the dialogue management system 122. For example, thedialogue management system 122 may access a user's implicit preferencessuch as the majority of the user's communication with the AI chatbotmodel has been through SMS messaging. Then the dialogue managementsystem 122 may, based on the implicit preference that the user typicallycommunicates with AI chatbot model through SMS messaging, select SMSmessaging to transmit a welcome message to the user or an answer touser's servicing intent.

The explicit preference may correspond to a user selection for aparticular channel of communication. For example, the user may convey toa customer representative via the phone or at a brick-and-mortarlocation an explicit preference for communicating via a mobileapplication text-based messaging. This may be done unprompted or afterbeing prompted by the customer representative as to what communicationchannel the user chooses. The customer representative may enter thisinformation into the customer representative device 150, which istransmitted to the dialogue management system 122. In other words, thedialogue management system 122 may receive the explicit preference inputdata from the customer representative device 150. In some embodiment'sthe explicit preference overrides any implicit preference or type ofinformation to be provided considerations. Thus, if the user has anexplicit preference to communicate via a mobile application messagingand the user has an implicit preference to communicate over SMSmessaging, the dialogue management system 122 would select thecommunication channel that corresponds to the user's explicitpreference. In other words, the dialogue management system 122 willdetermine whether it has received an explicit preference regarding thecurrent session and if not, the dialogue management system 122 will baseits selection on the user's implicit preference and/or the type ofinformation that the user is providing.

The dialogue management system 122 may select a particular channel basedon the type of information that the system is providing. For example, ifservicing intent is a request for three most recent transactions on acredit card or checking account, then the dialogue management system 122may select a visual communication medium (e.g., SMS messaging channel,mobile application messaging channel, email messaging channel, web-basedmessaging channel) over a voice-based communication medium (e.g., a AIchatbot phone call and an AI chatbot mobile application voicecommunication) because it is more difficult to convey such informationover voice. Although the user explicit preferences may trump the type ofcommunication considerations, the type of information considerations maytrump a user's implicit preferences. For example, if a user typicallycommunicates with the AI chatbot model via a phone call but theservicing intent is a request for an action that is difficult to conveyvia voice (e.g., a request for an account balance, recent transactions),the dialogue management system 122 may select a visual (text-based) AIchatbot model (e.g., select SMS messaging because the user's second mostused communication medium with the AI chatbot system).

If there are no implicit preferences, explicit preferences, then thedialogue management system 122 may default to selecting SMS messaging, amobile application text-based messaging if the user device has themobile application installed, or a voice-based messaging (e.g.,telephone call or a mobile application using voice-based communication(e.g., Alexa™ or Siri™).

In block 1018, the method 1000 includes transmitting, to the userdevice, a message via the selected messaging channel (e.g., SMSmessaging channel, mobile application messaging channel, email messagingchannel, web-based messaging channel, an AI chatbot phone call and an AIchatbot mobile application voice communication, etc.). The dialoguemanagement system 122 may in conjunction with the NLP system 124generate a message (e.g., a welcome message discussed above and/oranswer to the servicing intent).

FIG. 11 is a flowchart of a method 1100 for transitioning a user from atelephony or in person servicing to a voice-based AI chat session. Incertain example implementations, one or more of the steps of the method1100 may be performed by dialogue management system 122 using processor202 to execute memory 206 or the NLP system 124. In some embodiments,steps of method 900 may be delegated to other components, such as theuser device 102, and/or components associated with the system 100.Following method 1100, the system 100 may transmit a message to outputby or for display by, for example, user device 102.

In block 1102, the method 1100 includes receiving one or more userutterances associated with a user. The dialogue management system 122may receive one or more user utterances from a recording via a firstphone call, a user interaction device 140, a customer interaction device150.

In block 1104, the method 1100 includes generating an API call to an AIchatbot model. Blocks 416, 506, 610, 710, 812, 906, and 1004 are similarto block 1104, thus blocks 416, 506, 610, 710, 812, 906, and 1004descriptions are incorporated by reference herein.

In block 1106, the method 1100 includes transmitting the one or moreuser utterances to the AI chatbot model. The dialogue management system122 may transmit/transfer the one or more user utterances to the AIchatbot model (CBM 218) and the NLP system 124.

In block 1108, the method 1100 includes transcribing the one or moreuser utterances to a servicing intent by the AI chatbot model. The NLPsystem 124 with the AI chatbot model (CBM 218) may transcribe the one ormore user utterances.

In block 1110, the method 1100 includes mapping the transcribed one ormore user utterances to one or more servicing intent token(s) from aplurality of stored servicing intent tokens by the AI chatbot model assimilarly described with respect to blocks 420, 512, and 1010.

In optional block 1112, the method 1100 may include calling a userdevice associated with the user associated with the one or moreutterances. The method 1100 may not include this step when the userdevice is currently on a call with the dialogue management system 122such as with an IVR model.

In block 1114, the method 1100 includes providing, to the user device,an audio answer to the one or more of the servicing intent token(s). Insome embodiments, the dialogue management system 122 may communicatewith the user device via a phone call, a mobile application callfeature, or any other audio/voice messaging feature of the mobileapplication. As an example, the answer may be providing the user withthe user's account balance if the user requested their account balance.Other answers may be recent transactions of the user, a confirmationthat a bank card will be mailed, an explanation on why the recenttransaction was declined, or combinations thereof.

As used in this application, the terms “component,” “module,” “system,”“server,” “processor,” “memory,” and the like are intended to includeone or more computer-related units, such as but not limited to hardware,firmware, a combination of hardware and software, software, or softwarein execution. For example, a component may be, but is not limited tobeing, a process running on a processor, an object, an executable, athread of execution, a program, and/or a computer. By way ofillustration, both an application running on a computing device and thecomputing device can be a component. One or more components can residewithin a process and/or thread of execution and a component may belocalized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate by way of local and/or remote processessuch as in accordance with a signal having one or more data packets,such as data from one component interacting with another component in alocal system, distributed system, and/or across a network such as theInternet with other systems by way of the signal.

Certain embodiments and implementations of the disclosed technology aredescribed above with reference to block and flow diagrams of systems andmethods and/or computer program products according to exampleembodiments or implementations of the disclosed technology. It will beunderstood that one or more blocks of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, respectively, can be implemented by computer-executableprogram instructions. Likewise, some blocks of the block diagrams andflow diagrams may not necessarily need to be performed in the orderpresented, may be repeated, or may not necessarily need to be performedat all, according to some embodiments or implementations of thedisclosed technology.

These computer-executable program instructions may be loaded onto ageneral-purpose computer, a special-purpose computer, a processor, orother programmable data processing apparatus to produce a particularmachine, such that the instructions that execute on the computer,processor, or other programmable data processing apparatus create meansfor implementing one or more functions specified in the flow diagramblock or blocks. These computer program instructions may also be storedin a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement one or more functions specified in the flow diagram blockor blocks.

As an example, embodiments or implementations of the disclosedtechnology may provide for a computer program product, including acomputer-usable medium having a computer-readable program code orprogram instructions embodied therein, said computer-readable programcode adapted to be executed to implement one or more functions specifiedin the flow diagram block or blocks. Likewise, the computer programinstructions may be loaded onto a computer or other programmable dataprocessing apparatus to cause a series of operational elements or stepsto be performed on the computer or other programmable apparatus toproduce a computer-implemented process such that the instructions thatexecute on the computer or other programmable apparatus provide elementsor steps for implementing the functions specified in the flow diagramblock or blocks.

Accordingly, the block diagrams and flow diagrams support combinationsof means for performing the specified functions, combinations ofelements or steps for performing the specified functions, and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flow diagrams,and combinations of blocks in the block diagrams and flow diagrams, canbe implemented by special-purpose, hardware-based computer systems thatperform the specified functions, elements or steps, or combinations ofspecial-purpose hardware and computer instructions.

Certain implementations of the disclosed technology are described abovewith reference to user devices may include mobile computing devices.Those skilled in the art recognize that there are several categories ofmobile devices, generally known as portable computing devices that canrun on batteries but are not usually classified as laptops. For example,mobile devices can include, but are not limited to portable computers,tablet PCs, internet tablets, PDAs, ultra-mobile PCs (UMPCs), wearabledevices, and smart phones. Additionally, implementations of thedisclosed technology can be utilized with internet of things (IoT)devices, smart televisions and media devices, appliances, automobiles,toys, and voice command devices, along with peripherals that interfacewith these devices.

In this description, numerous specific details have been set forth. Itis to be understood, however, that implementations of the disclosedtechnology may be practiced without these specific details. In otherinstances, well-known methods, structures and techniques have not beenshown in detail in order not to obscure an understanding of thisdescription. References to “one embodiment,” “an embodiment,” “someembodiments,” “example embodiment,” “various embodiments,” “oneimplementation,” “an implementation,” “example implementation,” “variousimplementations,” “some implementations,” etc., indicate that theimplementation(s) of the disclosed technology so described may include aparticular feature, structure, or characteristic, but not everyimplementation necessarily includes the particular feature, structure,or characteristic. Further, repeated use of the phrase “in oneimplementation” does not necessarily refer to the same implementation,although it may.

Throughout the specification and the claims, the following terms take atleast the meanings explicitly associated herein, unless the contextclearly dictates otherwise. The term “connected” means that onefunction, feature, structure, or characteristic is directly joined to orin communication with another function, feature, structure, orcharacteristic. The term “coupled” means that one function, feature,structure, or characteristic is directly or indirectly joined to or incommunication with another function, feature, structure, orcharacteristic. The term “or” is intended to mean an inclusive “or.”Further, the terms “a,” “an,” and “the” are intended to mean one or moreunless specified otherwise or clear from the context to be directed to asingular form. By “comprising” or “containing” or “including” is meantthat at least the named element, or method step is present in article ormethod, but does not exclude the presence of other elements or methodsteps, even if the other such elements or method steps have the samefunction as what is named.

While certain embodiments of this disclosure have been described inconnection with what is presently considered to be the most practicaland various embodiments, it is to be understood that this disclosure isnot to be limited to the disclosed embodiments, but on the contrary, isintended to cover various modifications and equivalent arrangements ofthe various embodiments included within the scope of the appendedclaims. For example, it is to be understood that features of oneembodiment may not be exclusive to that embodiment and may be includedwith other embodiments. Although specific terms are employed herein,they are used in a generic and descriptive sense only and not forpurposes of limitation.

This written description uses examples to disclose certain embodimentsof the technology and also to enable any person skilled in the art topractice certain embodiments of this technology, including making andusing any apparatuses or systems and performing any incorporatedmethods. The patentable scope of certain embodiments of the technologyis defined in the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral language of the claims.

Exemplary Use Cases

The following example use cases are intended solely for explanatorypurposes, without limiting the scope of the disclosed technology.

In an example use case, a customer may have a question or requestassociated with an account they have with an organization. For example,the customer may want to know information related their account, such asthe account balance, due date for payment, specifics about a purchase,etc. In some instances, the customer may want to perform an actionrelated to their account, such as making a payment, dispute a charge,etc., and may further wish to have a convenient and efficient way topose his or her question or request account service without having todeal with the waiting times. Depending on the type of desired action andcustomer preferences, the customer may be transitioned to, for example,using text-based messaging as a form of communication for posingquestions or requesting service associated with their account.

To access the system 100, a user (e.g., the customer) using user device102 may call a phone number associated with the system 100 (e.g., thedialogue management system 122 of the system 100) so that the dialoguemanagement system 122 receives a phone call from the user device 102. Inturn, the dialogue management system 122 may ask the user for personallyidentifiable information such as the user's first name, last name, anddate of birth over the phone call. The dialogue management system 122may also inquire as to which account (e.g., bank account, credit cardaccount, loan account) the user is calling about. As the user answersthese inquiries, the dialogue management system 122 receives personallyidentifiable information from the user over the phone call. The dialoguemanagement system 122 may then authenticate the personally identifiableinformation by comparing the received personally identifiableinformation to stored personally identifiable information associatedwith the user. If the received personally identifiable informationmatches (e.g., beyond a predetermined 95% confidence threshold toaccount for “85” being provided as the year in the birth date in lieu of“1985,” for example) stored personally identifiable information, thenthe dialogue management system 122 (or some other part of the system100) authenticates the personally identifiable user. If, however, thereceived personally identifiable information does not match the storedpersonally identifiable information (e.g., beyond the predetermined 95%confidence threshold because the user's name fully matches a stored namebut the provided birth date is a different month than the stored birthdate), the dialogue management system 122 may request for additionalpersonally identifiable information or direct the user of user device102 to register (e.g., as a new user) with the dialogue managementsystem 122 online or via a mobile application. Alternatively, if thereceived personally identifiable information does not match any storedpersonally identifiable information the dialogue management system 122may, request to update some personally identifiable information, whichmay frequently arise if a current address is requested. Afterauthenticating the user, the dialogue management system 122 may generatean authentication token in response to authenticating the personallyidentifiable information.

The user may speak into the user device 102 to explain the reason forcalling (e.g., the user is requesting an account balance of theirsavings account) such that the dialogue management system 122 mayidentify the servicing intent of the user's request. Alternatively, thedialogue management system 122 may provide the user with a touch tonemenu (e.g., via the user device 102) or voice menu (e.g., via the userdevice 102) of options that it is prepared to help with. For example,the touch tone menu might state “Press or say ‘1’ for balance requests.Press ‘2’ to receive your most recent transactions.” Then, when the userpresses the number 1 on their user device 102 that corresponds withtheir desired servicing intent. For example, the user might press number1 corresponding to requesting a balance.

The dialogue management system 122 may generate a serving intent tokenbased on the received serving intent. The dialogue management system 122may generate an application programming interface (API) call to an AIchatbot model (e.g., CBM 218) associated with the identifiedcommunication channel. The dialogue management system 122 may transmit,to the AI chatbot model, the authentication token and the servicingintent token.

The dialogue management system 122 may map the servicing intent token toa stored servicing intent from a plurality of stored servicing intents.For example, the dialogue management system 122 may match the servicingintent token to the one stored servicing intent of the plurality ofstored servicing intents stored in customer information database 216 ordatabase 128. The dialogue management system 122 may transmitting awelcome message via an AI chat session, which may optionally identifythe user, confirm that the user has been authenticated, and/or reflectthe service intent, to the user device 102 via the chat session. Thewelcome message may simply be “Welcome to your chat session” in someembodiments. In other embodiments, the welcome message may state,“Welcome, John Smith! This chat session will provide your accountbalance for Account No. 1234 per your request. You have been previouslyauthenticated.” Alternatively, the dialogue management system 122 maysend an authentication message to the user device 102 separately fromthe welcome message, show at least a portion of the interface screen ortext in a color (e.g., green for authenticated, blue fornon-authenticated), or include an icon or symbol (e.g., green check marknext to “John Smith”) in lieu of an authentication message.Additionally, the dialogue management system 122 may transmit a voicemessage to the user device 102, via the phone call using IVR model 220,indicating that the AI chat session is available to ensure that the useris aware of the welcome message. Thus, the system 100 may seamlesslytransition the customer service session in the IVR call over to an AIchatbot session, and the AI chatbot model may continue the AI chatbotsession from that point without having to circle back to informationalready provided to the IVR model.

In another example use case, a user using user device 102 may call aphone number associated with the system 100 (e.g., the dialoguemanagement system 122) so that the dialogue management system 122receives a phone call from the user device 102. The dialogue managementsystem 122 may receive one or more user utterances. For example, the oneor more utterance may be “I would like to obtain an account balance ofmy savings account.” The dialogue management system 122 may transcribethe one or more utterances to text. The dialogue management system 122may generate an API call to an AI chatbot model (e.g., CBM 218). In lieuof or in addition to generating tokens in the prior example, thedialogue management system 122 may transmit, to the AI chatbot model, atleast one of the one or more user utterances (e.g., all may be providedor a filtered subset ignoring “hello” or other utterances that do notconvey customer information related to authentication or the firstcustomer request). The dialogue management system 122 may convert thetranscribed one or more user utterances to a servicing intentrecognizable by the AI chatbot model. The dialogue management system 122may transmits a message to the user device 102 via an AI chat session.In some use cases, the dialogue management system 122 may transmit, tothe user device 102, via the phone call, a voice message indicating thatthe AI chat session is available. Thus, the system 100 may seamlesslytransition the customer service session in the IVR call over to an AIchatbot session, and the AI chatbot model may continue the AI chatbotsession from that point without having to circle back to informationalready provided to the IVR model.

In another example use case, a user using user device 102 may call aphone number associated with the system 100 (e.g., dialogue managementsystem 122) so that the dialogue management system 122 receives a phonecall from the user device 102. The dialogue management system 122 mayreceive a touch tone phone input or a user utterance or both. Inresponse, the dialogue management system 122 may determine that thetouch tone phone input or the user utterance corresponds to a firstservicing intent. For example, the dialogue management system 122 maymap the touch tone phone input selected menu option (e.g., press 1 foraccount balance) to a stored servicing intent of the plurality of storedservicing intents. Alternatively, the dialogue management system 122 maymap the user utterance to a stored servicing intent of the plurality ofstored servicing intents (which may be generic to all models or specificto a particular model).

The dialogue management system 122 may generate a first servicing intenttoken based on the first servicing intent. The dialogue managementsystem 122 may generate an API call to an AI chatbot and transmit, tothe AI chatbot model, the first servicing intent token. The dialoguemanagement system 122 may transmit an SMS message, a mobile applicationnotification, a mobile application message, an email message, orcombinations thereof. In some use cases, the dialogue management system122 may transmit, to the user device via the first phone call, a firstvoice notification that the AI chat session is available. The dialoguemanagement system 122 may transmit, to the user device 102 via the AIchat session, a first answer responding to the first servicing intent.The dialogue management system 122 may receive from the user device 102via the AI chat session, a first user message comprising a secondservicing intent and a second user message comprising a request to betransferred to the IVR model 220. In turn, the dialogue managementsystem 122 may transmit the first user message to the IVR model 220. Thedialogue management system 122 may determine whether the first phonecall is active. If it is, then the dialogue management system 122 maytransmit a system message that the IVR model is available to the userdevice 102 via the AI chat session. If not, then the dialogue managementsystem 122 may initiate, via the IVR model 220, a second phone call withthe user device. The dialogue management system 122 may transmit, viathe first phone call or the second phone call, a second answerresponding to the second servicing intent. For example, the secondservicing intent may be a request for the three most recent transactionsmade for a checking account. Thus, the dialogue management system 122may vocally provide the three most recent transactions over the first orsecond phone call. In this way, the dialogue management system canseamlessly transition the user from an IVR model to a text-based model(AI chatbot model) and back to an IVR model in response to a userrequest (e.g., the user starts on the phone, finds it easier to receiveinformation associated with a first request via text, and then hops inhis car and needs to switch back to the phone without suffering theinconvenience of starting an entirely new chat after each transition).Each model is provided with the prior messages and servicing intents sothat the user does not have to repeat himself when the user changes fromthe voice-based IVR model 220 to the text-based model 218 (AI chatbotmodel).

What is claimed is:
 1. A system for transitioning an artificialintelligence (AI) chat session to a telephony-based call, the systemcomprising: one or more processors; and a memory in communication withthe one or more processors and storing instructions that, when executedby the one or more processors, are configured to cause the system to:receive, from a user device via an AI chat session, servicing intentinput data and a first selection of a first user input object configuredto initiate a telephony-based call; generate a token based on theservicing intent input data; transmit a message to the user device viathe AI chat session, the message associated with the token; receive,from the user device via the AI chat session, a second selection of asecond user input object indicating a preference to initiate thetelephony-based call associated with the token; and cause the userdevice to initiate the telephony-based call associated with the token.2. The system of claim 1, wherein generating the token comprises mappingthe servicing intent input data to a plurality of stored tokens.
 3. Thesystem of claim 1, wherein the first and second user input objects eachcomprise a button configured for selection within a mobile application.4. The system of claim 1, wherein the instructions are furtherconfigured to cause the system to: receive, from the user device,personally identifiable information; determine whether the receivedpersonally identifiable information matches stored personallyidentifiable information associated with a user of the user device; andresponsive to determining the received personally identifiableinformation matches the stored personally identifiable information,authenticate the user.
 5. The system of claim 4, wherein theinstructions are further configured to cause the system to: responsiveto authenticating the user: generate an authentication token; andassociate the authentication token with the telephony-based call.
 6. Thesystem of claim 1, wherein the instructions are further configured tocause the system to: provide the token to a voice system; and cause theuser device to initiate the telephony-based call via the voice system.7. The system of claim 1, wherein initiating the telephony-based callcomprises connecting a user of the user device to a customer serviceagent associated with the token.
 8. A system for transitioning anartificial intelligence (AI) chat session to a telephony-based call, thesystem comprising: one or more processors; and a memory in communicationwith the one or more processors and storing instructions that, whenexecuted by the one or more processors, are configured to cause thesystem to: receive, from a user device via an AI chat session, servicingintent input data; responsive to receiving the servicing intent inputdata, generate a one-time code; receive, from the user device via the AIchat session, a first selection of a first user input object indicatinga preference to initiate a telephony-based call associated with theone-time code; and cause the user device to initiate the telephony-basedcall.
 9. The system of claim 8, wherein the one-time code is based onthe servicing intent input data.
 10. The system of claim 8, wherein theinstructions are further configured to cause the system to: receive,from the user device, personally identifiable information; determinewhether the received personally identifiable information matches storedpersonally identifiable information associated with a user of the userdevice; and responsive to determining the received personallyidentifiable information matches the stored personally identifiableinformation, authenticate the user by associating the one-time code withthe user.
 11. The system of claim 8, wherein the instructions arefurther configured to cause the system to: provide the one-time code toa voice system; and cause the user device to initiate thetelephony-based call via the voice system.
 12. The system of claim 8,wherein the telephony-based call is associated with the one-time code.13. The system of claim 8, wherein initiating the telephony-based callcomprises connecting a user of the user device to a customer serviceagent associated with the one-time code.
 14. A system comprising: one ormore processors; and a memory in communication with the one or moreprocessors and storing instructions that, when executed by the one ormore processors, are configured to cause the system to: monitor, via amobile application, user activity associated with a user device;generate a code based on the user activity; receive, from the userdevice via the mobile application, a first selection of a first userinput object indicating a preference to initiate a phone call; andresponsive to receiving the first selection, transmit the generated codeto the user device to initiate the phone call associated with thegenerated code.
 15. The system of claim 14, wherein monitoring the useractivity comprises conducting a dialogue with a user of the user device.16. The system of claim 14, wherein the code corresponds to a servicingintent of a user of the user device.
 17. The system of claim 14, whereinthe first user input object comprises a button.
 18. The system of claim14, wherein the instructions are further configured to cause the systemto: provide the generated code to a voice system, wherein initiating thephone call comprises initiating the phone call via the voice system. 19.The system of claim 14, wherein initiating the phone call comprisesconnecting a user of the user device to a customer service agentassociated with the generated code.
 20. The system of claim 14, whereininitiating the phone call comprises appending the generated code to aphone number associated with a financial institution.